tag:blogger.com,1999:blog-171746742024-03-13T14:08:34.846+01:00....more semantic!...just a few words about life, the universe, and research on topics related to the semantic webBiblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comBlogger192125tag:blogger.com,1999:blog-17174674.post-51460120133629962862014-12-04T17:00:00.000+01:002014-12-04T19:42:29.177+01:00The Fact Ranking Challenge continues....<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjqwXnCiIfo_s8VtE9PiOBfgX-xDLRx6k14alKtbFqcQ689vTg8DvEs31DN1h7Kvoot6j7ap8_MWdSAM09TbaJOOZ3Hnpun0nk6deVCiV7iBt1P12iv7bmrSZzorn7tLZlbGb_UDA/s1600/spaceage.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjqwXnCiIfo_s8VtE9PiOBfgX-xDLRx6k14alKtbFqcQ689vTg8DvEs31DN1h7Kvoot6j7ap8_MWdSAM09TbaJOOZ3Hnpun0nk6deVCiV7iBt1P12iv7bmrSZzorn7tLZlbGb_UDA/s1600/spaceage.jpg" height="247" width="320" /></a></div>
Yes, you might remember our experiment with fact ranking [1,2,3,4]. After almost 4 months, this is the current state of intermediate results (cf. below).<br />
<br />
To make it short: YES, there has been some progress. NO, it is not sufficient so far.<br />
<br />
Therefore, PLEASE HELP!<br />
If you have already started to work with the application [3], PLEASE CONTINUE!<br />
If you don't know the application [3], PLEASE START!<br />
PLEASE PARTICIPATE!<br />
<br />
<br />
<br />
We know that is is not an easy task and we also know that it takes time. Therefore, WE REALLY DO APPRECIATE YOUR HELP VERY MUCH!!<br />
<br />
Please keep on playing and help us to gather more data!<br />
Please tell all your family and friends to support us!<br />
Please tell all your colleagues and fellow students to support us!<br />
<br />
THANK YOU VERY MUCH!<br />
<br />
[1] <a href="http://s16a.org/fr/">The Fact Ranking Quiz Application</a>, http://s16a.org/fr/<br />
[2] <a href="http://moresemantic.blogspot.de/2014/07/help-us-with-research-problem.html">Help us with a Research Problem</a>, July 30, 2014<br />
[3] <a href="http://moresemantic.blogspot.de/2014/08/the-importance-of-relevance.html">The Importance of Relevance - Intermediate Results</a>, Aug 19, 2014<br />
[4] <a href="http://moresemantic.blogspot.de/2014/09/more-intermediate-results-from-our-fact.html">More intermediate Results from our Fact Ranking Challenge</a>, Sep. 2, 2014<br />
<br />
<br />
...and here are the statistics:<br />
<br />
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Number of users who participated: 465</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Sum of concepts done: 2410</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">485 unique concepts are covered (out of 541). </span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Average concepts done per user: 5.183</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">295 times concepts were skipped (relevance in Step2 hasn't been changed for any of the facts).</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">CONCEPTS DONE:</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">0 concepts were done by 97 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">1 concepts were done by 93 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">2 concepts were done by 78 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">3 concepts were done by 49 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">4 concepts were done by 33 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">5 concepts were done by 26 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">6 concepts were done by 10 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">7 concepts were done by 7 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">8 concepts were done by 9 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">9 concepts were done by 8 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">10 concepts were done by 8 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">11 concepts were done by 6 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">12 concepts were done by 2 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">13 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">14 concepts were done by 5 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">15 concepts were done by 3 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">16 concepts were done by 3 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">17 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">18 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">19 concepts were done by 2 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">20 concepts were done by 3 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">21 concepts were done by 2 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">23 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">24 concepts were done by 2 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">25 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">26 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">31 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">36 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">40 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">42 concepts were done by 2 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">56 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">58 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">60 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">64 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">68 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">70 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">88 concepts were done by 1 users. </span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">201 concepts were done by 1 users. </span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">EDUCATION:</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">highschool : 47 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">phd : 71 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">other : 26 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">bachelors : 105 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">masters : 213 users.</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">AGE:</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">33+ : 261 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">19-25 : 72 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">26-32 : 122 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">0-18 : 7 users.</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">GENDER:</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">female : 111 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">male : 351 users.</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">COUNTRY OF ORIGIN:</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Angola : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Belarus : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Portugal : 4 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Philippines : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Morocco : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Greece : 5 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Ukraine : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Indonesia : 4 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Afghanistan : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Sri Lanka : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Italy : 15 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Iraq : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">India : 44 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">France : 14 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Denmark : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Latvia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Pakistan : 4 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Syrian Arab Republic : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Montenegro : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Armenia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Mexico : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Canada : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Brazil : 10 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Venezuela : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Croatia : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Macedonia, The Former Yugoslav Republic of : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Romania : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Western Sahara : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Algeria : 5 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Sweden : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">United States : 24 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Serbia : 10 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Nigeria : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Estonia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Spain : 9 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Taiwan, Republic of China : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Ireland : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Russian Federation : 12 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Israel : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Colombia : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Switzerland : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Azerbaijan : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Kenya : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Yemen : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Malaysia : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Viet Nam : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Australia : 5 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Peru : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Albania : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">South Africa : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Tunisia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Netherlands : 9 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">China : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Somalia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Slovenia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Finland : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Lithuania : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Austria : 7 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Sudan : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">United Kingdom : 15 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Egypt : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Bahamas : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Hungary : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Belgium : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Poland : 6 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Iran, Islamic Republic of : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Bulgaria : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Norway : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Germany : 177 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">New Zealand : 3 users.</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">COUNTRY OF RESIDENCE:</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">United Arab Emirates : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">null : 0 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Belarus : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Portugal : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Philippines : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Morocco : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Greece : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Ukraine : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Indonesia : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Luxembourg : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Sri Lanka : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Italy : 14 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Iraq : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">India : 32 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">France : 17 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Jordan : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Denmark : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Latvia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Pakistan : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Syrian Arab Republic : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Oman : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Turkey : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Czech Republic : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Armenia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Canada : 4 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Brazil : 9 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Croatia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Romania : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Algeria : 6 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Sweden : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">United States : 25 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Serbia : 5 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Nigeria : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Saudi Arabia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Estonia : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Spain : 7 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Taiwan, Republic of China : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Ireland : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Russian Federation : 6 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Israel : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Colombia : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Switzerland : 8 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Azerbaijan : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Kenya : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Norfolk Island : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Yemen : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Malaysia : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Australia : 7 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Peru : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Albania : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">South Africa : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Netherlands : 13 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">China : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Somalia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Slovenia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Gambia : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Finland : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Lithuania : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Austria : 6 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">United Kingdom : 17 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Egypt : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Bahamas : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Belgium : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Poland : 7 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Singapore : 2 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Iran, Islamic Republic of : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Bulgaria : 3 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Norway : 1 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Germany : 197 users.</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">New Zealand : 3 users.</span></div>
<div class="p2">
<br /></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">Overall average confidence of users about the seen concepts: 2.616</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">NO. OF USERS PER CONCEPT:</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">1 users for 67 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">2 users for 123 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">3 users for 108 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">4 users for 86 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">5 users for 73 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">6 users for 37 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">7 users for 11 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">8 users for 7 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">9 users for 2 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">10 users for 2 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">18 users for 1 concepts</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">On AVERAGE there are 3.398 users per concept.</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">NO. OF ANSWERS PER CONCEPT (STEP 1):</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">On AVERAGE there are 5.002 answers per concept.</span></div>
<div class="p2">
<span style="font-family: Verdana, sans-serif; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">NONSENSE STATEMENTS:</span></div>
<div class="p1">
<span style="font-family: Verdana, sans-serif; font-size: x-small;">TOTAL number of nonsense sentences = 1771</span></div>
<div class="p2">
<br /></div>
<br />
<div class="p3">
<br /></div>
Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-75711592421895356502014-12-04T13:13:00.004+01:002014-12-04T13:13:30.438+01:00The Flipped Classroom Experiment - First Lessons Learned<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgtH10cZfIyOv0AImjhqIT3ma0LVhdrOdG0ebCyaTLUxWmmTefLw037kQVjftWWmbRptg8ooB7HMekMKPRg58q_SrgQaGaf9UWPyDXlnTQ1v1mUFPT9xhbepxVQ9fROVVffhb1AUw/s1600/Munster1550MonstersShips.gif" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgtH10cZfIyOv0AImjhqIT3ma0LVhdrOdG0ebCyaTLUxWmmTefLw037kQVjftWWmbRptg8ooB7HMekMKPRg58q_SrgQaGaf9UWPyDXlnTQ1v1mUFPT9xhbepxVQ9fROVVffhb1AUw/s1600/Munster1550MonstersShips.gif" height="237" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Sailing in previously unknown seas...</td></tr>
</tbody></table>
As I already had announced in the previous post entitled "<a href="http://moresemantic.blogspot.de/2014/10/and-now-for-something-completely.html">And Now for Something Completely Different...</a>" this winter semester the Semantic Web Technologies lecture does not take place like an ordinary lecture. We follow the so-called Flipped Classroom Concept, i.e. the students "prepare" for the lecture (by watching the video recordings from the previous semesters), and we do the lecture in a more interactive way, driven by the needs of the students. So far the theory, but does it also hold in practice?
<br />
OK, now we have reached lecture No. 6, i.e. almost already half of the entire course. Let me tell you what happened so far.<br />
<br />
<ol>
<li><b>Lecture preparation</b>: I have to admit, I was a little bit afraid whether it would really work. But, the students always seem to be very well prepared and I'm really happy about that!</li>
<li><b>Interaction</b>: Well, I have learned that it is me, who has to take over the incentive. Now, the game works as follows: every week, I publish so-called syllabus questions, i.e. essential questions that the students should be able to answer after they have understood the lecture. We start the lecture now always by going through these syllabus questions, which give us sufficient material for interactivity (=discussion).<br /><br />When I simply ask "<i>Do you have any questions about the content of the lecture?</i>", I rarely receive any answers. But, when I start to talk about some of the issues from the content, more and more people are contributing.</li>
<li><b>Feedback</b>: Well, according to the answers I receive for the syllabus questions, I can immediately recognize where potential problems might be and we can talk about it. For this, I usually keep the lecture slides at hand for visual support. I can then repeat what is important and can dive deeper into explanations as well as to give more examples<br /><br />Actually, I have asked the students whether they like the way of this lecture, and I have not received any negative answer so far.</li>
</ol>
<div>
What I have learned so far is that for me as a lecturer the effort is almost quite the same for the flipped classroom as for a 'traditional lecture'. Moreover, you have to be very well prepared, because you always encourage the students to ask all kind of questions. The nice thing is the direct feedback that guides you to exactly those points where more explanation is needed. Personally, I like this way of the lecture very much. I never had any other lecture so far (well, besides lab courses or seminars) with so much feedback from the students. </div>
<div>
<br /></div>
<div>
...and of course I would also like to thank all of my students that they are willing to undergo this experiment together with me! :)</div>
<div>
<br /></div>
Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-11801676340645992522014-10-14T10:46:00.001+02:002014-10-14T10:46:39.405+02:00And now for something completely different...<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVSmqR6BO3-td0mPYmSHLcprliHWfpgIiWc0JAigrMlN4xzage3Cin6FfNDPcp3vsvrJNn3BIN7Wyus5AViXsBT_zLlMWzFFo5hCEkxUc7frEGsWnNukZavX8UkB5cj0Ck3NpUjg/s1600/Bildschirmfoto+2014-10-14+um+10.40.05.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVSmqR6BO3-td0mPYmSHLcprliHWfpgIiWc0JAigrMlN4xzage3Cin6FfNDPcp3vsvrJNn3BIN7Wyus5AViXsBT_zLlMWzFFo5hCEkxUc7frEGsWnNukZavX8UkB5cj0Ck3NpUjg/s1600/Bildschirmfoto+2014-10-14+um+10.40.05.jpg" height="240" width="320" /></a></div>
As always in October, lectures are starting again. Like every year, I will give a lecture on Semantic Web Technologies. BTW, I have realized that I give now courses on Semantic Web for almost 10 years. It all started as a seminar at the Friedrich-Schiller Universität back in Jena and became a fully-fledged lecture here at the HPI in Potsdam. Like the <a href="http://www.tele-task.de/archive/series/overview/971/">lecture of last winter semester</a>, almost all lectures have been recorded and are online available either at <a href="http://www.tele-task.de/">tele-Task</a> or <a href="http://www.yovisto.com/">yovisto</a>.<br />
<br />
Moreover, we have also prepared two MOOC courses <a href="https://open.hpi.de/courses/semanticweb">Semantic Web Technologies </a>in Spring 2013, and <a href="https://open.hpi.de/courses/semanticweb2014">Knowledge Engineering with Semantic Web Technologies</a> in Spring 2014, both very successful with thousand(s) of students.<br />
<br />
This semester, I have decided not to do the very same all over again and to try out something completely different...<br />
<br />
Have you ever heard of the <a href="http://en.wikipedia.org/wiki/Flipped_classroom">Flipped Classroom</a> concept? This semester, we are going to turn the lecture situation around for the students. All the lecture content has already been recorded. Thus, students can prepare for each lecture at home by watching the videos and studying the handouts as well as the course materials. Then, in the classroom, I will not present the content again, but we are going to discuss<br />
<br />
<ul>
<li>everything which needs more attention according to the students,</li>
<li>everything that the students did not quite well understand,</li>
<li>including all problems, errors, and complements that seem to be important.</li>
</ul>
Thus, to follow the (live) lecture the students have to prepare accordingly. Of course this will only work with the active participation of the students. On the other hand, it will also be more challenging for the lecturer and the tutors, because we have to be very well prepared to deal with all kind of potential questions and problems. Of course we will work out problem solutions and answers always together with the students. And it will be also the students who will take over the lead - well of course under the lecturer's guidance.<br />
<br />
I'm very curious whether this concept will work out well with my lecture here at HPI. Please keep your fingers crossed and I will keep you posted.<br />
<br />
<b>Additional Links:</b><br />
<br />
<ul>
<li>The <a href="http://semweb2015.blogspot.de/">Blog for our lecture 'Semantic Web Technologies'</a>, winter semester 2014/15</li>
</ul>
Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-49073830981654578052014-09-02T10:11:00.001+02:002014-09-02T10:11:29.757+02:00More intermediate Results from our Fact Ranking Challenge<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhTupD2pobaysBdf5kGsQqaCFC9oq5BCPrrWRP8NRTZFYqsOX3xAqxnSIkjXDu9sBZ3EmVztmSlHmO6FnHb_POzBqMNHB1RvSt0IoiYxBJeexSQ6Nkys8CbtRTNlXvWGjlPDKShrA/s1600/Bildschirmfoto+2014-09-02+um+10.07.49.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhTupD2pobaysBdf5kGsQqaCFC9oq5BCPrrWRP8NRTZFYqsOX3xAqxnSIkjXDu9sBZ3EmVztmSlHmO6FnHb_POzBqMNHB1RvSt0IoiYxBJeexSQ6Nkys8CbtRTNlXvWGjlPDKShrA/s1600/Bildschirmfoto+2014-09-02+um+10.07.49.jpg" height="242" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fact Ranking Challenge</td></tr>
</tbody></table>
Again, here is an update about our currently gathered data about our fact ranking experiment.<br />
<br />
<a href="http://moresemantic.blogspot.de/2014/07/help-us-with-research-problem.html">We started the original challenge about 5 weeks ago</a> and now are able to present you some more intermediate results [1]. Nevertheless, the challenge is still running. <b><a href="http://s16a.org/fr/">Therefore, please distribute, participate, advertise, and help us to generate a fully fledged ground truth for fact ranking</a> [2]</b>. All data will be made publicly available for further research.
<br />
<br />
To determine the importance of a fact is of utmost importance, if you want to properly understand the content of information. Usually, you have a rich variety of possible interpretations of information. To determine the proper interpretation, you are going to use the context, i.e. further available information. So, the question develops from "what is important?" to "what is important with regard of this specific context?".
<br />
<br />
<b>Current Intermediate Statistics (from Aug 28):</b>
<br />
<br />
<div class="p1">
Number of users who participated: 388</div>
<div class="p2">
<br /></div>
<div class="p1">
Sum of concepts done: 1736</div>
<div class="p2">
<br /></div>
<div class="p1">
446 unique concepts are covered (out of 541). </div>
<div class="p2">
<br /></div>
<div class="p1">
Average concepts done per user: 4.47</div>
<div class="p2">
<br /></div>
<div class="p1">
CONCEPTS DONE:</div>
<div class="p1">
0 concepts were done by 79 users. </div>
<div class="p1">
1 concepts were done by 79 users. </div>
<div class="p1">
2 concepts were done by 68 users. </div>
<div class="p1">
3 concepts were done by 41 users. </div>
<div class="p1">
4 concepts were done by 25 users. </div>
<div class="p1">
5 concepts were done by 22 users. </div>
<div class="p1">
6 concepts were done by 8 users. </div>
<div class="p1">
7 concepts were done by 7 users. </div>
<div class="p1">
8 concepts were done by 9 users. </div>
<div class="p1">
9 concepts were done by 6 users. </div>
<div class="p1">
10 concepts were done by 8 users. </div>
<div class="p1">
11 concepts were done by 4 users. </div>
<div class="p1">
12 concepts were done by 2 users. </div>
<div class="p1">
13 concepts were done by 1 users. </div>
<div class="p1">
14 concepts were done by 4 users. </div>
<div class="p1">
15 concepts were done by 3 users. </div>
<div class="p1">
16 concepts were done by 2 users. </div>
<div class="p1">
19 concepts were done by 1 users. </div>
<div class="p1">
20 concepts were done by 3 users. </div>
<div class="p1">
21 concepts were done by 2 users. </div>
<div class="p1">
22 concepts were done by 1 users. </div>
<div class="p1">
23 concepts were done by 1 users. </div>
<div class="p1">
24 concepts were done by 2 users. </div>
<div class="p1">
25 concepts were done by 1 users. </div>
<div class="p1">
31 concepts were done by 1 users. </div>
<div class="p1">
38 concepts were done by 2 users. </div>
<div class="p1">
40 concepts were done by 1 users. </div>
<div class="p1">
42 concepts were done by 1 users. </div>
<div class="p1">
58 concepts were done by 1 users. </div>
<div class="p1">
59 concepts were done by 1 users. </div>
<div class="p1">
62 concepts were done by 1 users. </div>
<div class="p1">
64 concepts were done by 1 users. </div>
<div class="p2">
<br /></div>
<div class="p1">
EDUCATION:</div>
<div class="p1">
highschool : 41 users.</div>
<div class="p1">
phd : 57 users.</div>
<div class="p1">
other : 22 users.</div>
<div class="p1">
bachelors : 93 users.</div>
<div class="p1">
masters : 174 users.</div>
<div class="p2">
<br /></div>
<div class="p1">
AGE:</div>
<div class="p1">
33+ : 216 users.</div>
<div class="p1">
19-25 : 68 users.</div>
<div class="p1">
26-32 : 98 users.</div>
<div class="p1">
0-18 : 5 users.</div>
<div class="p2">
<br /></div>
<div class="p1">
GENDER:</div>
<div class="p1">
female : 90 users.</div>
<div class="p1">
male : 297 users.</div>
<div class="p2">
<br /></div>
<div class="p1">
COUNTRY OF ORIGIN:</div>
<div class="p1">
Angola : 1 users.</div>
<div class="p1">
Belarus : 1 users.</div>
<div class="p1">
Portugal : 4 users.</div>
<div class="p1">
Philippines : 2 users.</div>
<div class="p1">
Morocco : 3 users.</div>
<div class="p1">
Greece : 5 users.</div>
<div class="p1">
Ukraine : 3 users.</div>
<div class="p1">
Indonesia : 3 users.</div>
<div class="p1">
Sri Lanka : 1 users.</div>
<div class="p1">
Italy : 13 users.</div>
<div class="p1">
Iraq : 1 users.</div>
<div class="p1">
India : 40 users.</div>
<div class="p1">
France : 11 users.</div>
<div class="p1">
Latvia : 1 users.</div>
<div class="p1">
Pakistan : 3 users.</div>
<div class="p1">
Syrian Arab Republic : 1 users.</div>
<div class="p1">
Montenegro : 1 users.</div>
<div class="p1">
Armenia : 1 users.</div>
<div class="p1">
Mexico : 2 users.</div>
<div class="p1">
Brazil : 10 users.</div>
<div class="p1">
Venezuela : 1 users.</div>
<div class="p1">
Croatia : 2 users.</div>
<div class="p1">
Macedonia, The Former Yugoslav Republic of : 1 users.</div>
<div class="p1">
Romania : 2 users.</div>
<div class="p1">
Western Sahara : 1 users.</div>
<div class="p1">
Algeria : 4 users.</div>
<div class="p1">
Sweden : 2 users.</div>
<div class="p1">
United States : 21 users.</div>
<div class="p1">
Serbia : 8 users.</div>
<div class="p1">
Nigeria : 2 users.</div>
<div class="p1">
Estonia : 1 users.</div>
<div class="p1">
Spain : 8 users.</div>
<div class="p1">
Taiwan, Republic of China : 2 users.</div>
<div class="p1">
Ireland : 1 users.</div>
<div class="p1">
Israel : 1 users.</div>
<div class="p1">
Russian Federation : 9 users.</div>
<div class="p1">
Colombia : 3 users.</div>
<div class="p1">
Switzerland : 1 users.</div>
<div class="p1">
Azerbaijan : 1 users.</div>
<div class="p1">
Kenya : 2 users.</div>
<div class="p1">
Yemen : 1 users.</div>
<div class="p1">
Malaysia : 2 users.</div>
<div class="p1">
Viet Nam : 1 users.</div>
<div class="p1">
Australia : 4 users.</div>
<div class="p1">
Peru : 1 users.</div>
<div class="p1">
Albania : 1 users.</div>
<div class="p1">
South Africa : 2 users.</div>
<div class="p1">
Netherlands : 8 users.</div>
<div class="p1">
China : 2 users.</div>
<div class="p1">
Somalia : 1 users.</div>
<div class="p1">
Slovenia : 1 users.</div>
<div class="p1">
Finland : 3 users.</div>
<div class="p1">
Lithuania : 1 users.</div>
<div class="p1">
Austria : 6 users.</div>
<div class="p1">
Sudan : 1 users.</div>
<div class="p1">
United Kingdom : 15 users.</div>
<div class="p1">
Egypt : 2 users.</div>
<div class="p1">
Bahamas : 1 users.</div>
<div class="p1">
Hungary : 1 users.</div>
<div class="p1">
Poland : 4 users.</div>
<div class="p1">
Iran, Islamic Republic of : 2 users.</div>
<div class="p1">
Bulgaria : 3 users.</div>
<div class="p1">
Norway : 1 users.</div>
<div class="p1">
Germany : 140 users.</div>
<div class="p1">
New Zealand : 3 users.</div>
<div class="p2">
<br /></div>
<div class="p1">
COUNTRY OF RESIDENCE:</div>
<div class="p1">
United Arab Emirates : 1 users.</div>
<div class="p1">
Belarus : 1 users.</div>
<div class="p1">
Portugal : 2 users.</div>
<div class="p1">
Philippines : 1 users.</div>
<div class="p1">
Morocco : 2 users.</div>
<div class="p1">
Greece : 3 users.</div>
<div class="p1">
Ukraine : 1 users.</div>
<div class="p1">
Indonesia : 2 users.</div>
<div class="p1">
Luxembourg : 1 users.</div>
<div class="p1">
Sri Lanka : 1 users.</div>
<div class="p1">
Italy : 11 users.</div>
<div class="p1">
India : 31 users.</div>
<div class="p1">
France : 13 users.</div>
<div class="p1">
Jordan : 1 users.</div>
<div class="p1">
Denmark : 1 users.</div>
<div class="p1">
Latvia : 1 users.</div>
<div class="p1">
Pakistan : 3 users.</div>
<div class="p1">
Oman : 1 users.</div>
<div class="p1">
Turkey : 1 users.</div>
<div class="p1">
Czech Republic : 1 users.</div>
<div class="p1">
Armenia : 1 users.</div>
<div class="p1">
Canada : 3 users.</div>
<div class="p1">
Brazil : 9 users.</div>
<div class="p1">
Croatia : 1 users.</div>
<div class="p1">
Romania : 3 users.</div>
<div class="p1">
Algeria : 4 users.</div>
<div class="p1">
Sweden : 3 users.</div>
<div class="p1">
United States : 23 users.</div>
<div class="p1">
Serbia : 4 users.</div>
<div class="p1">
Nigeria : 1 users.</div>
<div class="p1">
Saudi Arabia : 1 users.</div>
<div class="p1">
Estonia : 2 users.</div>
<div class="p1">
Spain : 7 users.</div>
<div class="p1">
Taiwan, Republic of China : 1 users.</div>
<div class="p1">
Ireland : 3 users.</div>
<div class="p1">
Israel : 2 users.</div>
<div class="p1">
Russian Federation : 2 users.</div>
<div class="p1">
Colombia : 2 users.</div>
<div class="p1">
Switzerland : 7 users.</div>
<div class="p1">
Azerbaijan : 2 users.</div>
<div class="p1">
Kenya : 2 users.</div>
<div class="p1">
Norfolk Island : 1 users.</div>
<div class="p1">
Yemen : 1 users.</div>
<div class="p1">
Malaysia : 2 users.</div>
<div class="p1">
Australia : 6 users.</div>
<div class="p1">
Peru : 1 users.</div>
<div class="p1">
Albania : 1 users.</div>
<div class="p1">
South Africa : 1 users.</div>
<div class="p1">
Netherlands : 12 users.</div>
<div class="p1">
Somalia : 1 users.</div>
<div class="p1">
Slovenia : 1 users.</div>
<div class="p1">
Gambia : 1 users.</div>
<div class="p1">
Finland : 3 users.</div>
<div class="p1">
Lithuania : 1 users.</div>
<div class="p1">
Austria : 5 users.</div>
<div class="p1">
United Kingdom : 17 users.</div>
<div class="p1">
Egypt : 1 users.</div>
<div class="p1">
Bahamas : 1 users.</div>
<div class="p1">
Belgium : 2 users.</div>
<div class="p1">
Poland : 5 users.</div>
<div class="p1">
Singapore : 1 users.</div>
<div class="p1">
Iran, Islamic Republic of : 1 users.</div>
<div class="p1">
Bulgaria : 3 users.</div>
<div class="p1">
Norway : 1 users.</div>
<div class="p1">
Germany : 154 users.</div>
<div class="p1">
New Zealand : 2 users.</div>
<div class="p2">
<br /></div>
<div class="p1">
Overall confidence of users about the seen concepts: 2.687</div>
<div class="p2">
<br /></div>
<div class="p1">
NO. OF USERS PER CONCEPT:</div>
<div class="p1">
On AVERAGE there are 2.18 users per concept.</div>
<div class="p2">
<br /></div>
<div class="p1">
NO. OF ANSWERS PER CONCEPT (STEP 1):</div>
<div class="p1">
On AVERAGE there are 4.71 answers per concept.</div>
<div class="p2">
<br /></div>
<div class="p1">
NONSENSE STATEMENTS:</div>
<br />
<div class="p1">
TOTAL number of nonsense sentences = 1371</div>
<br />
<br />
<i><span style="color: red;">Hint: You might wonder about the impressive high scores on the top of the list? Well, actually points are given exponentially, i.e. the longer you play, the more points you will score per processed concept.</span></i><br />
<i><br /></i><b>
References:</b><br />
[1] <a href="http://moresemantic.blogspot.de/2014/07/help-us-with-research-problem.html">Help Us with a Research Problem</a>, July 30, 2914<br />
[2] <a href="http://s16a.org/fr/">Fact Ranking Web-Application</a>, http://s16a.org/fr/Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-28577502278818088692014-08-19T12:38:00.000+02:002014-08-19T13:26:10.017+02:00The Importance of Relevance - Intermediate Results<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRfrj6BxH8BgweQW3BkQRF5OjmRQ3lboUbbadxKovuRzOTRuXnTwRdXRUajKDd2EfUQAWyC9C-8m8W49znb6phkdrWXVQKs9zxzuHyfjA3mUYE57UC5tZ6JCK6iSft-Z1aNjYnYA/s1600/Bildschirmfoto+2014-08-19+um+12.32.34.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRfrj6BxH8BgweQW3BkQRF5OjmRQ3lboUbbadxKovuRzOTRuXnTwRdXRUajKDd2EfUQAWyC9C-8m8W49znb6phkdrWXVQKs9zxzuHyfjA3mUYE57UC5tZ6JCK6iSft-Z1aNjYnYA/s1600/Bildschirmfoto+2014-08-19+um+12.32.34.jpg" height="173" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Current Highscore List of our fact ranking challenge</td></tr>
</tbody></table>
In my last post, we invited you to take part in our research challenge, which was about creating a ground truth for fact ranking algorithms. To determine the importance of a fact is of utmost importance, if you want to properly understand the content of information. Usually, you have a rich variety of possible interpretations of information. To determine the proper interpretation, you are going to use the context, i.e. further available information. So, the question develops from "what is important?" to "what is important with regard of this specific context?".
<br />
<br />
<a href="http://moresemantic.blogspot.de/2014/07/help-us-with-research-problem.html">We started the original challenge about 3 weeks ago</a> and now are able to present you first intermediate results [1]. Nevertheless, the challenge is still running. <b><a href="http://s16a.org/fr/">Therefore, please distribute, participate, advertise, and help us to generate a fully fledged ground truth for fact ranking</a> [2]</b>. All data will be made publicly available for further research.
<br />
<br />
<b>Current Intermediate Statistics:
</b>
<br />
Number of users who participated: 110 <b>(Thanks to all you you!!!)</b><br />
Number of overall processed concepts: 509<br />
Overall 200 unique concepts are covered (out of 541).<br />
<br />
Average concepts processed per user: 4.63<br />
<br />
<b>Detailed number of processed concepts per user:</b><br />
0 concepts were done by 15 users.<br />
1 concepts were done by 27 users.<br />
2 concepts were done by 21 users.<br />
3 concepts were done by 12 users.<br />
4 concepts were done by 7 users.<br />
5 concepts were done by 8 users.<br />
6 concepts were done by 2 users.<br />
7 concepts were done by 1 users.<br />
8 concepts were done by 4 users.<br />
9 concepts were done by 1 users.<br />
10 concepts were done by 2 users.<br />
11 concepts were done by 2 users.<br />
14 concepts were done by 1 users.<br />
16 concepts were done by 1 users.<br />
20 concepts were done by 1 users.<br />
22 concepts were done by 1 users.<br />
25 concepts were done by 1 users.<br />
31 concepts were done by 1 users. <br />
53 concepts were done by 2 users.<br />
<br />
<b>Participant statistics:</b><br />
<br />
EDUCATION:<br />
highschool : 7 users.<br />
bachelors : 28 users.<br />
masters : 47 users.<br />
phd : 23 users.<br />
<div>
other : 5 users. </div>
<div>
<br /></div>
AGE:<br />
33+ : 43 users.<br />
26-32 : 34 users.<br />
19-25 : 33 users.<br />
<div>
<br /></div>
GENDER:<br />
female : 25 users.<br />
male : 85 users.<br />
<br />
COUNTRY OF ORIGIN:<br />
United States : 9 users.<br />
Serbia : 6 users.<br />
Spain : 2 users.<br />
Ukraine : 1 users.<br />
Russian Federation : 3 users.<br />
Colombia : 1 users.<br />
Italy : 6 users.
India : 2 users.<br />
France : 3 users.<br />
Malaysia : 1 users.<br />
Australia : 1 users.<br />
Albania : 1 users.<br />
China : 1 users.<br />
Pakistan : 1 users.<br />
Finland : 1 users.<br />
Austria : 2 users.<br />
Montenegro : 1 users.<br />
United Kingdom : 8 users.<br />
Brazil : 3 users.<br />
Poland : 2 users.<br />
Iran, Islamic Republic of : 1 users.<br />
Macedonia, The Former Yugoslav Republic of : 1 users.<br />
Croatia : 1 users.<br />
Germany : 49 users.<br />
Algeria : 1 users.<br />
New Zealand : 1 users.<br />
Sweden : 1 users.
<br />
<br />
Overall confidence of users about the seen concepts: 2.585<br />
<br />
NO. OF USERS PER CONCEPT:<br />
On AVERAGE there are 1.295 users per concept.<br />
<br />
NO. OF ANSWERS PER CONCEPT (STEP 1):<br />
On AVERAGE there are 4.825 answers per concept.
<br />
<br />
<b>We will keep you posted about the results.</b><br />
<b>Please distribute, participate, advertise, and help us to generate a fully fledged ground truth for fact ranking</b>.<br />
<br />
<i>Hint: You might wonder about the impressive high scores on the top of the list? Well, actually points are given exponentially, i.e. the longer you play, the more points you will score per processed concept.</i><br />
<i><br /></i><b>
References:</b><br />
[1] <a href="http://moresemantic.blogspot.de/2014/07/help-us-with-research-problem.html">Help Us with a Research Problem</a>, July 30, 2914<br />
[2] <a href="http://s16a.org/fr/">Fact Ranking Web-Application</a>, http://s16a.org/fr/Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-12705813325826072792014-07-30T15:16:00.002+02:002014-07-30T15:16:42.329+02:00Help Us with a Research Problem<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhXa7QCHw8WGLS89K1frUwSVtL_F1F7k-_IaM_PPzNztGAy4K1K46UDdnB3XD6C6N4Y2D-g5HXC20YdaSFqNuN2ow9X7uFikZ91B4LFLignXqP-3hTXJRiiJfieF4HvQ4cnrC2UrQ/s1600/Bildschirmfoto+2014-07-30+um+14.53.47.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhXa7QCHw8WGLS89K1frUwSVtL_F1F7k-_IaM_PPzNztGAy4K1K46UDdnB3XD6C6N4Y2D-g5HXC20YdaSFqNuN2ow9X7uFikZ91B4LFLignXqP-3hTXJRiiJfieF4HvQ4cnrC2UrQ/s1600/Bildschirmfoto+2014-07-30+um+14.53.47.jpg" height="148" width="320" /></a></div>
As you might know, we already have tried previously to let the public participate in our research. Last time, we have had developed several games (with a purpose). This time, unfortunately it is not a game, simply because the development of a good game is really expensive. But, let's get to the point. What is the task all about, where you can help us....?
<br />
<br />
You know, my research group is working on semantic technologies. Semantics in that sense means, we are trying to (automatically) understand what information (or data) is all about and what is the meaning of it. Sometimes, information is ambiguous. This makes it difficult to understand, because you have to solve ambiguities with the help of context.<br />
<br />
On the other hand, sometimes you have various different information about a subject. How do you determine, which information or fact is more important or relevant than another? Just a quick example. Let's assume we have the following two facts:<br />
<br />
(1) Albert Einstein is a physicist.<br />
(2) Albert Einstein is a Vegetarian.<br />
<br />
Which of the two facts is more important or relevant? Yes, this is difficult to answer, simply because the truth often lies in the eye of the beholder. For a vegetarian, maybe the second fact is more important. But, what about the most common opinion? What would the mainstream think? Probably, most people would say that fact (1) in general is more important.<br />
<br />
So, what we are doing is that we develop heuristics that determine the importance of facts (relative to other facts). To get an idea about the quality of our heuristics, we have to do an evaluation, i.e. somebody has to decide whether the decision of the heuristics was wrong or right. Unfortunately, there does not exist a ground truth for this task called "<b>fact ranking</b>". Therefore, we are about to create a new ground truth that later will be publicly available and open for all researchers.<br />
<br />
This ground truth is achieved with <a href="http://s16a.org/fr/">the little 'voting' application that you will find here</a> [1]. You just have to register with the tool and then the task will be explained to you in detail. We took 500 popular concepts from Wikipedia and you have (1) to think about the most important facts about these concepts that come to your mind and then (2) rate the (new) facts presented to you according to their relevance. There is no right or wrong answer. Just vote as you think it seems right for you. Afterwards, we will aggregate all votes from all participants to determine the general (mainstream) relevance of the presented facts.<br />
<br />
You might interrupt your rating of the presented facts at any time you like and continue later. To make it a bit more interesting, you can also <b>score points </b>and of course there is a<b> highscore </b>list.
We would really appreciate your help in this task. Please do also spread the word. The more participants, the more valid our ground truth will be.<br />
<br />
We know that this is a difficult and sometimes rather boring task. The more we would be really grateful for your assistance!<br />
<br />
[1] <a href="http://s16a.org/fr/">Fact Ranking Web-Application</a>, http://s16a.org/fr/Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-52828256006054531172014-07-30T11:01:00.001+02:002014-07-30T11:08:31.080+02:00...in Times of WarFor us Western Europeans, war always seems to be far, far away in some other country (or some distant times in the past). Usually, we read about it in the newspapers or see the pictures in the media. But, we are not concerned directly. This also includes me as a researcher. Of course, we also have students in our institute who come from countries or regions in crisis. But, they are here and the crisis is there... elsewhere.<br />
<br />
As you might know, I recently finished my OpenHPI online course '<a href="https://open.hpi.de/courses/2d1ede48-4cc6-4a36-bcc4-6cb02e36b3ea">Knowledge Engineering with Semantic Web Technologies</a>'. The course was rather popular with a total of 4,623 enrolled students from all over the world. 611 students took part in the final course exam and 450 students have finished the course successfully (yeah!!).<br />
<br />
Of course the means of interaction with the students are limited in an online course. You follow the stream of discussions in the OpenHPI platform, answering a question here and then. Sometimes you also receive email from one of the course participants...<br />
<br />
Today, I have received email from a course participant in Gaza, Palestine. He wrote me about his appreciation for the OpenHPI team to offer courses like this and about the projects he carried out during his University studies. Unfortunately, as he wrote, due to the current situation in Gaza, infrastructure has been destroyed including power outages as well as network failure. Of course this makes it difficult next to impossible to continue the course (not to speak about all the other major problems for the people that arise from this conflict). I am deeply impressed that in a situation like this, people still continue their efforts to invest in their education...and their future.<br />
<br />
And yes, war has finally also knocked on the door of our small island of the fortunate...<br />
<br />Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-67250711478741558882014-07-16T10:50:00.000+02:002014-07-16T10:50:39.750+02:00New DBpedia Graph Statistics<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmCj1xld8yWCpDqvfHo2DM7nTvLRNkwiLdbLjU6uf_yefzK7vDPEblOHvJmif1eAsp5AR-qwH7jeSG346NT8h4YlvW7OJ6SOlB1l06RQTUEbXvN8Gm7wM6h5wFZ-uiul7mQdKpYw/s1600/dbpedia_grafo.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmCj1xld8yWCpDqvfHo2DM7nTvLRNkwiLdbLjU6uf_yefzK7vDPEblOHvJmif1eAsp5AR-qwH7jeSG346NT8h4YlvW7OJ6SOlB1l06RQTUEbXvN8Gm7wM6h5wFZ-uiul7mQdKpYw/s1600/dbpedia_grafo.png" /></a></div>
Recently, we have been working on the DBpedia / Wikipedia Page Link dataset. We have considered the English and the German language versions for this project. In the current DBpedia 3.9 page links English and German datasets 18 million and 6 million entities are represented respectively. But the original DBpedia only contains about 4 million and 1 million distinct entities for English and German versions.
This significant difference is mainly due to the current DBpedia pagelinks dataset include redirect pages and pagelinks with resources that are not considered as entites (as e.g. thumbnails and other images). So we considered cleaning up DBpedia pagelinks dataset for the computation of statistical parameters (a.g. pagerank or HITS). For the Cleanup we have removed all unnecessary and redundant RDF-Triples from the pagelinks dataset, i.e all removing the redirect pages (Redirection pages are just URIs that automatically forward a user to another Wikipedia page, but do not represent entities) as well as RDF-Triples representing resources that do not have an own <span style="font-family: Courier New, Courier, monospace;">rdfs:label</span> (as per DBpedia documentation every entity has an <span style="font-family: Courier New, Courier, monospace;">rdfs:label </span>reference).
<br />
<br />
One of the benefits of the cleaned up pagelink dataset is the faster computation of statistical graph measures (while not influencing the overall statistics, i.e. redirect pages usually don't have incoming links and theother removed resources (as e.g. images) don't have outgoing links). Based on this dataset we have computed PageRank, Hub and Authorities (HITS), PageInlink Counts and PageOutLink Counts. Please find the details of the datasets here on our research group's webpage [1].
<br />
<br />
For Computation of the DBpedia graph statistics we have used JUNG — the Java Universal Network/Graph Framework. Please find the source code for PageRank and HITS computation here via GitHub [2].
<br />
<br />
<b>References and further Reading:</b><br />
[1] <a href="http://semanticmultimedia.org/node/6">New PageRank Computations for DBpedia 3.9 (English/German) at SemanticMultimedia</a>
<br />
[2] <a href="https://github.com/SemanticMultimedia/JungGraphMeasures">Source code for DBpedia Graph Statistics</a>Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comHasso-Plattner-Institut, Prof.-Dr.-Helmert-Straße 2-3, 14482 Potsdam, Germany52.3941245 13.13338569999996352.389279499999994 13.123300699999962 52.3989695 13.143470699999963tag:blogger.com,1999:blog-17174674.post-31160981324310128052014-07-14T13:13:00.002+02:002014-07-14T13:13:22.355+02:00Harald's Original Miscellany - More Truth about Football - Part 4<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhLLE63bRo77o-8lf6TTpDpc9-MwQkQHIVKUny6XBju6sGXwRQ4ZB2za89f2UksXLpeZ8Ts2oDPgUcnZohEPrE-F-FkQnX6y7usNLb2GJjvPvKIjK48V5glVo_fa82KtTpPTH9shA/s1600/football1870s.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhLLE63bRo77o-8lf6TTpDpc9-MwQkQHIVKUny6XBju6sGXwRQ4ZB2za89f2UksXLpeZ8Ts2oDPgUcnZohEPrE-F-FkQnX6y7usNLb2GJjvPvKIjK48V5glVo_fa82KtTpPTH9shA/s1600/football1870s.jpg" height="200" width="168" /></a></div>
Finally, Germany has won the Soccer Worldcup 2014. Therefore, also our little statistics on soccer will come to an end with the post today. You might ask yourself what kind of data is left for soccer players in Wikipedia and DBpedia. Well, unfortunately only a little. But, we will try to make something out of it. Last time, we've ask for the <a href="http://moresemantic.blogspot.de/2014/07/haralds-original-miscellany-more-truth.html">number of team changes and the correlation to popularity vs. scored goals for soccer players</a>. What is left, if we look at the available data?
<br />
<br />
<br />
<br />
<br />
<br />
We have the data about the years in which the football players were active or have played in their national soccer team. Let's start with the national team years [1]:
<br />
<br />
<table align="center" border="1" class="sparql">
<tbody>
<tr>
<th>nationalyears</th>
<th>NumPlayers</th>
</tr>
<tr>
<td>8</td>
<td>3</td>
</tr>
<tr>
<td>7</td>
<td>20</td>
</tr>
<tr>
<td>6</td>
<td>85</td>
</tr>
<tr>
<td>5</td>
<td>371</td>
</tr>
<tr>
<td>4</td>
<td>1070</td>
</tr>
<tr>
<td>3</td>
<td>2479</td>
</tr>
<tr>
<td>2</td>
<td>6116</td>
</tr>
<tr>
<td>1</td>
<td>28211</td>
</tr>
</tbody></table>
<br />
Well, it was obvious that the most players have only 1 or two years in the national team. But, there are exceptional players who achieved even 8 years. But, who are these long term players? [2]:<br />
<div>
<br /></div>
<table align="center" border="1" class="sparql">
<tbody>
<tr>
<th>nationalyears</th>
<th>Player</th>
<th>Team</th>
</tr>
<tr>
<td>8</td>
<td>Wojciech Łobodziński</td>
<td>"Poland"@en</td>
</tr>
<tr>
<td>8</td>
<td>Wojciech Łobodziński</td>
<td>"Poland Under 16"@en</td>
</tr>
<tr>
<td>8</td>
<td>Wojciech Łobodziński</td>
<td>"Poland Under 17"@en</td>
</tr>
<tr>
<td>8</td>
<td>Wojciech Łobodziński</td>
<td>"Poland Under 21"@en</td>
</tr>
<tr>
<td>8</td>
<td>Wojciech Łobodziński</td>
<td>"Poland Under 18"@en</td>
</tr>
<tr>
<td>8</td>
<td>Santiago Cañizares</td>
<td>"Spain Under-17"@en</td>
</tr>
<tr>
<td>8</td>
<td>Santiago Cañizares</td>
<td>"Spain Under-16"@en</td>
</tr>
<tr>
<td>8</td>
<td>Santiago Cañizares</td>
<td>"Spain Under-21"@en</td>
</tr>
<tr>
<td>8</td>
<td>Santiago Cañizares</td>
<td>"Spain Under-18"@en</td>
</tr>
<tr>
<td>8</td>
<td>Santiago Cañizares</td>
<td>"Spain Under-23"@en</td>
</tr>
<tr>
<td>8</td>
<td>Santiago Cañizares</td>
<td>"Spain Under-19"@en</td>
</tr>
<tr>
<td>8</td>
<td>Santiago Cañizares</td>
<td>"Spain Under-20"@en</td>
</tr>
<tr>
<td>7</td>
<td>Aydın Yılmaz</td>
<td>"Turkey Under-21"@en</td>
</tr>
<tr>
<td>7</td>
<td>Aydın Yılmaz</td>
<td>"Turkey"@en</td>
</tr>
<tr>
<td>7</td>
<td>Aydın Yılmaz</td>
<td>"Turkey"@en</td>
</tr>
<tr>
<td>7</td>
<td>Aydın Yılmaz</td>
<td>"Turkey Under-19s"@en</td>
</tr>
<tr>
<td>7</td>
<td>Aydın Yılmaz</td>
<td>"Turkey Under-17"@en</td>
</tr>
<tr>
<td>7</td>
<td>Aydın Yılmaz</td>
<td>"Turkey A2"@en</td>
</tr>
<tr>
<td>7</td>
<td>Ismael Urzaiz</td>
<td>"Spain Under-17"@en</td>
</tr>
<tr>
<td>7</td>
<td>Ismael Urzaiz</td>
<td>"Spain Under-16"@en</td>
</tr>
</tbody></table>
<br />
<div>
Possibly you will never have heard of Poland's <a href="http://en.wikipedia.org/wiki/Wojciech_%C5%81obodzi%C5%84ski">Wojciech Łobodziński </a>or Spain's <a href="http://en.wikipedia.org/wiki/Santiago_Ca%C3%B1izares">Santiago Cañizares</a>. Here another flaw in the data becomes visible. There is no such thing as the unique national team. We have "under 16", "under 17", "under 18", and so on... So you start your career already with 15 and after 8 years you would be 23 and possibly be in the "real" national team.<br />
<div>
<br /></div>
<div>
Let's have a look at the active years of players. Unfortunately, here the data is rather messy [3]: </div>
<table align="center" border="1" class="sparql">
<tbody>
<tr>
<th>person</th>
<th>From</th>
<th>To</th>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Marta_(footballer)">http://dbpedia.org/resource/Marta_(footballer)</a></td>
<td>2000</td>
<td>9223372036854775807</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Carlos_Alberto_Gomes_de_Lima">http://dbpedia.org/resource/Carlos_Alberto_Gomes_de_Lima</a></td>
<td>2006</td>
<td>200720082008</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Blake_Camp">http://dbpedia.org/resource/Blake_Camp</a></td>
<td>2008</td>
<td>200420052006</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Birgit_Prinz">http://dbpedia.org/resource/Birgit_Prinz</a></td>
<td>1998</td>
<td>200320042005</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Dejan_Damjanovi%C4%87">http://dbpedia.org/resource/Dejan_Damjanovi%C4%87</a></td>
<td>1998</td>
<td>20112012</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Inka_Grings">http://dbpedia.org/resource/Inka_Grings</a></td>
<td>1995</td>
<td>20092010</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Breno_Silva">http://dbpedia.org/resource/Breno_Silva</a></td>
<td>2003</td>
<td>20092010</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Vin%C3%ADcius_Calamari">http://dbpedia.org/resource/Vin%C3%ADcius_Calamari</a></td>
<td>2007</td>
<td>20092010</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Samuel_Jos%C3%A9_da_Silva_Vieira">http://dbpedia.org/resource/Samuel_Jos%C3%A9_da_Silva_Vieira</a></td>
<td>1994</td>
<td>20082009</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Chad_Marshall">http://dbpedia.org/resource/Chad_Marshall</a></td>
<td>2004</td>
<td>20082009</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Michael_Parkhurst">http://dbpedia.org/resource/Michael_Parkhurst</a></td>
<td>2003</td>
<td>20072008</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Geison_Rodrigues_Marrote">http://dbpedia.org/resource/Geison_Rodrigues_Marrote</a></td>
<td>2004</td>
<td>20072008</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Dejan_Stankovi%C4%87">http://dbpedia.org/resource/Dejan_Stankovi%C4%87</a></td>
<td>1994</td>
<td>20062010</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Glauber_Da_Silva">http://dbpedia.org/resource/Glauber_Da_Silva</a></td>
<td>2001</td>
<td>20062007</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Hugo_Sarmiento">http://dbpedia.org/resource/Hugo_Sarmiento</a></td>
<td>1999</td>
<td>20032007</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Obafemi_Martins">http://dbpedia.org/resource/Obafemi_Martins</a></td>
<td>2000</td>
<td>20032004</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Heslley_Couto">http://dbpedia.org/resource/Heslley_Couto</a></td>
<td>2005</td>
<td>20032006</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Dalibor_Filipovi%C4%87">http://dbpedia.org/resource/Dalibor_Filipovi%C4%87</a></td>
<td>1992</td>
<td>20022003</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Dmytro_Zayko">http://dbpedia.org/resource/Dmytro_Zayko</a></td>
<td>2005</td>
<td>20022004</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Aviv_Volnerman">http://dbpedia.org/resource/Aviv_Volnerman</a></td>
<td>1998</td>
<td>20012004</td>
</tr>
</tbody></table>
<div>
<br />
Possibly this is because of people not only writing single year's into the Wikipedia infoboxes, but time spans and other things. In the list above are only the Top 20. Just remove the LIMIT from the SPARQL query and further down you will find more valid data.<br />
<br />
OK, let's come to our last problem related to football. Is there a correlation between the height of a player (simply because we have that data) and the number of achieved goals [4]?<br />
<table align="center" border="1" class="sparql">
<tbody>
<tr>
<th>Height</th>
<th>sumgoals</th>
</tr>
<tr>
<td>243.84</td>
<td>1</td>
</tr>
<tr>
<td>23.0</td>
<td>59</td>
</tr>
<tr>
<td>215.9</td>
<td>1</td>
</tr>
<tr>
<td>206.0</td>
<td>0</td>
</tr>
<tr>
<td>204.0</td>
<td>0</td>
</tr>
<tr>
<td>203.2</td>
<td>24</td>
</tr>
<tr>
<td>203.2</td>
<td>47</td>
</tr>
<tr>
<td>203.0</td>
<td>0</td>
</tr>
<tr>
<td>203.0</td>
<td>0</td>
</tr>
<tr>
<td>203.0</td>
<td>51</td>
</tr>
<tr>
<td>202.0</td>
<td>19</td>
</tr>
<tr>
<td>202.0</td>
<td>0</td>
</tr>
<tr>
<td>202.0</td>
<td>15</td>
</tr>
<tr>
<td>201.0</td>
<td>20</td>
</tr>
<tr>
<td>201.0</td>
<td>0</td>
</tr>
<tr>
<td>201.0</td>
<td>0</td>
</tr>
<tr>
<td>200.66</td>
<td>11</td>
</tr>
<tr>
<td>200.66</td>
<td>48</td>
</tr>
<tr>
<td>200.66</td>
<td>0</td>
</tr>
<tr>
<td>200.66</td>
<td>0</td>
</tr>
<tr>
<td>200.66</td>
<td>0</td>
</tr>
<tr>
<td>200.66</td>
<td>124</td>
</tr>
<tr>
<td>200.66</td>
<td>146</td>
</tr>
<tr>
<td>200.66</td>
<td>0</td>
</tr>
<tr>
<td>200.66</td>
<td>1</td>
</tr>
<tr>
<td>200.66</td>
<td>16</td>
</tr>
<tr>
<td>200.66</td>
<td>0</td>
</tr>
<tr>
<td>200.66</td>
<td>102</td>
</tr>
<tr>
<td>200.66</td>
<td>6</td>
</tr>
<tr>
<td>200.66</td>
<td>0</td>
</tr>
<tr>
<td>200.0</td>
<td>0</td>
</tr>
<tr>
<td>200.0</td>
<td>20</td>
</tr>
<tr>
<td>199.0</td>
<td>0</td>
</tr>
<tr>
<td>199.0</td>
<td>0</td>
</tr>
<tr>
<td>199.0</td>
<td>78</td>
</tr>
<tr>
<td>199.0</td>
<td>47</td>
</tr>
<tr>
<td>199.0</td>
<td>0</td>
</tr>
<tr>
<td>199.0</td>
<td>62</td>
</tr>
<tr>
<td>199.0</td>
<td>32</td>
</tr>
<tr>
<td>199.0</td>
<td>84</td>
</tr>
</tbody></table>
<br />
It is rather difficult to recognize something in this data. You see heights and the number of goals that player of that height have scored. Fascinating that there seem to be a significant number of players that are taller than 2 meters. I guess that the list leader with 2.43 meters is just incorrect data. Now. this is so many data that we have to visualize it to recognize something....<br />
<br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEipezccPb8FDacdhroPBI2ZyGyne5P3919ku2lcBl2NQ4lOG5xzwl9KWFw7T2wmErRTa-ulLtcyjwNzSOdkY_Hugly2wIeWxesZXV9tkKoouv2Y_GlCPrmy8fQKV2OwyUqq8nXJvA/s1600/Soccer-Correllation-01.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEipezccPb8FDacdhroPBI2ZyGyne5P3919ku2lcBl2NQ4lOG5xzwl9KWFw7T2wmErRTa-ulLtcyjwNzSOdkY_Hugly2wIeWxesZXV9tkKoouv2Y_GlCPrmy8fQKV2OwyUqq8nXJvA/s1600/Soccer-Correllation-01.jpg" height="216" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Correlation between height of soccer players and scored goals</td></tr>
</tbody></table>
In the diagram to the left you see the soccer players height (x-axis) vs. the number of scored goals (y-axis). Interesting thing to notice is that the heights approximately show a Gaussian distribution, i.e most players have a "middle" height, on the extremes there are only a few. Well, there seems to be an exception. on the outer left you will notice a large fraction of players with a height of 1.52m with goal scores ranging among 0 to 300. This is extraordinary, because I have no idea what kind of group this is or if there is simply an error in the data again. What I noticed is that among this group there seems to be a larger fraction of female Asian soccer players. Maybe they are responsible for that large number of outliers, but this requires further investigation.<br />
<br />
Alas, I want to add one last table. For each player there is the information on which position she or he is playing. Of course Wikipedia authors are far from any sort of agreement how to name the player's position. Thus, there is a rather huge variety. Nevertheless, I will leave you with the table to make any sense from it. Enjoy [5]:<br />
<table align="center" border="1" class="sparql">
<tbody>
<tr>
<th>position</th>
<th>number</th>
<th>avheight</th>
<th>avgoals</th>
</tr>
<tr>
<td>midfielder</td>
<td>4450</td>
<td>176.822828644929299</td>
<td>18.077078651685393</td>
</tr>
<tr>
<td>defender</td>
<td>3429</td>
<td>181.37523034450506</td>
<td>8.058326042578011</td>
</tr>
<tr>
<td>striker</td>
<td>2643</td>
<td>180.169761559433703</td>
<td>65.213015512674991</td>
</tr>
<tr>
<td>goalkeeper</td>
<td>2086</td>
<td>186.186064557569709</td>
<td>0.174496644295302</td>
</tr>
<tr>
<td>forward</td>
<td>1787</td>
<td>178.865346269495465</td>
<td>46.388919977616116</td>
</tr>
<tr>
<td>centre back</td>
<td>786</td>
<td>185.7391350821381</td>
<td>10.651399491094148</td>
</tr>
<tr>
<td>winger</td>
<td>648</td>
<td>174.69354896725695</td>
<td>27.583333333333333</td>
</tr>
<tr>
<td>attacking midfielder</td>
<td>490</td>
<td>175.897877222177931</td>
<td>38.006122448979592</td>
</tr>
<tr>
<td>left back</td>
<td>461</td>
<td>177.661735253323698</td>
<td>6.995661605206074</td>
</tr>
<tr>
<td>defensive midfielder</td>
<td>427</td>
<td>179.741452037869347</td>
<td>11.978922716627635</td>
</tr>
<tr>
<td>right back</td>
<td>405</td>
<td>177.829456545982828</td>
<td>7.301234567901235</td>
</tr>
<tr>
<td>central defender</td>
<td>196</td>
<td>185.005408462213008</td>
<td>8.897959183673469</td>
</tr>
<tr>
<td>central midfielder</td>
<td>161</td>
<td>178.223105294363834</td>
<td>20.099378881987578</td>
</tr>
<tr>
<td>full back</td>
<td>158</td>
<td>174.659366173080242</td>
<td>6.759493670886076</td>
</tr>
<tr>
<td>centre forward</td>
<td>140</td>
<td>179.09685701642717</td>
<td>80.792857142857143</td>
</tr>
<tr>
<td>left winger</td>
<td>113</td>
<td>174.790176753449224</td>
<td>28.398230088495575</td>
</tr>
<tr>
<td>defender, midfielder</td>
<td>102</td>
<td>179.789412255380659</td>
<td>10.607843137254902</td>
</tr>
<tr>
<td>inside forward</td>
<td>96</td>
<td>172.094789346059145</td>
<td>64.083333333333333</td>
</tr>
<tr>
<td>defender/midfielder</td>
<td>82</td>
<td>179.178292995545911</td>
<td>11.463414634146341</td>
</tr>
<tr>
<td>defender / midfielder</td>
<td>79</td>
<td>180.188100887250282</td>
<td>15.050632911392405</td>
</tr>
<tr>
<td>left-back</td>
<td>65</td>
<td>175.642922504131603</td>
<td>7.692307692307692</td>
</tr>
<tr>
<td>right winger</td>
<td>62</td>
<td>175.247418803553424</td>
<td>30.096774193548387</td>
</tr>
<tr>
<td>centre-back</td>
<td>56</td>
<td>521.327500479561941</td>
<td>9.375</td>
</tr>
<tr>
<td>right-back</td>
<td>54</td>
<td>176.101110952871815</td>
<td>6.111111111111111</td>
</tr>
<tr>
<td>midfielder/forward</td>
<td>52</td>
<td>172.013075924836657</td>
<td>22.557692307692308</td>
</tr>
<tr>
<td>midfield</td>
<td>52</td>
<td>176.982691251314588</td>
<td>30.673076923076923</td>
</tr>
<tr>
<td>defender (retired)</td>
<td>50</td>
<td>181.37344055175781</td>
<td>14.48</td>
</tr>
<tr>
<td>second striker</td>
<td>48</td>
<td>174.673332850138346</td>
<td>63.854166666666667</td>
</tr>
<tr>
<td>full-back</td>
<td>44</td>
<td>175.908408771861666</td>
<td>2.681818181818182</td>
</tr>
<tr>
<td>striker / winger</td>
<td>41</td>
<td>178.235609845417295</td>
<td>49.219512195121951</td>
</tr>
</tbody></table>
<br />
Of course I limited the table to 30 rows. Interestingly, the average height of goalkeepers is larger than for midfielders or strikers. As expected, strikers on the average score more goals than defenders or goalkeepers.<br />
<br /></div>
<div>
<br /></div>
<div>
<b>SPARQL Queries and original data:</b></div>
<div>
[1] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0ASELECT+%3Fnationalyears+COUNT%28*%29+as+%3FNumPlayers+WHERE%0D%0A%7B%0D%0ASELECT+DISTINCT+%3Fperson+COUNT%28%3Fyear%29+as+%3Fnationalyears+WHERE%0D%0A%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+%3B%0D%0A+++++++++dbpprop%3Anationalyears+%3Fyear+.%0D%0A%7D%0D%0AGROUP+by+%3Fperson%0D%0A%7D%0D%0AORDER+BY+DESC+%28%3Fnationalyears%29%0D%0ALIMIT+100&format=text%2Fhtml&timeout=30000&debug=on">number of players wrt. to years in the national team</a></div>
<div>
[2] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0ASELECT+%3Fnationalyears+xsd%3Astring%28%3Fpname%29+as+%3FPlayer+%3Ftname+as+%3FTeam+WHERE%0D%0A%7B%0D%0A+%3Fperson+rdfs%3Alabel+%3Fpname+FILTER+%28lang%28%3Fpname%29%3D%22en%22%29+.%0D%0A+%3Fperson+dbpprop%3Anationalteam+%3Fteam+.%0D%0A+%3Fteam+foaf%3Aname+%3Ftname+.%0D%0A%7B%0D%0ASELECT+DISTINCT+%3Fperson+COUNT%28%3Fyear%29+as+%3Fnationalyears+WHERE%0D%0A%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+%3B%0D%0A+++++++++dbpprop%3Anationalyears+%3Fyear+.%0D%0A%7D%0D%0AGROUP+by+%3Fperson%0D%0A%7D%0D%0A%7D%0D%0AORDER+BY+DESC+%28%3Fnationalyears%29%0D%0ALIMIT+20&format=text%2Fhtml&timeout=30000&debug=on">player name and team name of national team players with the longest playing terms</a><br />
[3] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0A%0D%0ASELECT+DISTINCT+%3Fperson+MIN%28xsd%3Ainteger%28%3Fyear%29%29+AS+%3FFrom+MAX%28xsd%3Ainteger%28%3Fyear%29%29+AS+%3FTo+WHERE%0D%0A%7B%0D%0A+%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+%3B%0D%0A+++++++++dbpprop%3Ayears+%3Fyear+FILTER+%28isNUMERIC%28%3Fyear%29%29.%0D%0A%7D%0D%0AGROUP+by+%3Fperson%0D%0AORDER+BY+DESC+%28MAX%28xsd%3Ainteger%28%3Fyear%29%29-MIN%28xsd%3Ainteger%28%3Fyear%29%29%29%0D%0ALIMIT+20%0D%0A&format=text%2Fhtml&timeout=30000&debug=on">players ordered by number of active years</a><br />
[4] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0ASELECT+xsd%3Afloat%28%3Fcmheight%29+as+%3FHeight+%3Fsumgoals%0D%0AFROM+%3Chttp%3A%2F%2Fdbpedia.org%3E%0D%0AWHERE+%7B%0D%0A++%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+.%0D%0A++%3Fperson+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2FPerson%2Fheight%3E+%3Fcmheight+FILTER+%28%3Fperson+%21%3D+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2FSanjiban_Ghosh%3E%29+.%0D%0A%7B+SELECT+%3Fperson+SUM%28%3Fgoals%29+as+%3Fsumgoals+WHERE+%7B%0D%0A++%3Fperson+dbpprop%3Agoals+%3Fgoals+FILTER+%28isNUMERIC%28%3Fgoals%29%29+FILTER+%28%3Fperson+%21%3D+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2FAlcindo_Sartori%3E+%29.%0D%0A+%7D+GROUP+BY+%3Fperson%0D%0A%7D%0D%0A%7D+ORDER+by+DESC%28%3Fcmheight%29%0D%0ALIMIT+40&format=text%2Fhtml&timeout=30000&debug=on">is there a correlation between soccer player height and scored goals?</a><br />
[5] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0ASELECT+lcase%28xsd%3Astring%28%3Fnationalteam%29%29+as+%3Fposition+COUNT%28*%29+as+%3Fnumber+AVG%28xsd%3Afloat%28%3Fcmheight%29%29+as+%3Favheight+AVG%28%3Fsumgoals%29+as+%3Favgoals%0D%0AFROM+%3Chttp%3A%2F%2Fdbpedia.org%3E%0D%0AWHERE+%7B%0D%0A++%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+.%0D%0A++%3Fperson+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2FPerson%2Fheight%3E+%3Fcmheight+FILTER+%28%3Fperson+%21%3D+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2FSanjiban_Ghosh%3E%29+.%0D%0A++%3Fperson+dbpedia-owl%3Aposition+%3Fnationalteam+.%0D%0A%7B+SELECT+%3Fperson+SUM%28%3Fgoals%29+as+%3Fsumgoals+WHERE+%7B%0D%0A++%3Fperson+dbpprop%3Agoals+%3Fgoals+FILTER+%28isNUMERIC%28%3Fgoals%29%29+FILTER+%28%3Fperson+%21%3D+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2FAlcindo_Sartori%3E+%29.%0D%0A+%7D+GROUP+BY+%3Fperson%0D%0A%7D%0D%0A%7DGROUP+BY+lcase%28xsd%3Astring%28%3Fnationalteam%29%29%0D%0AORDER+BY+DESC%28COUNT%28*%29%29%0D%0ALIMIT+30&format=text%2Fhtml&timeout=30000&debug=on">is there any correlation between player position, height, and scored goals?</a></div>
<div>
</div>
</div>
Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comPotsdam, Germany52.3905689 13.06447290000005552.2356559 12.741749400000055 52.5454819 13.387196400000056tag:blogger.com,1999:blog-17174674.post-6791250445120439012014-07-03T18:46:00.004+02:002014-07-06T09:26:11.977+02:00Harald's Original Miscellany - More Truth about Football - Part 3<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhB9W4V6rrzZ0CHVVUd72e5jPhsmQ4KtZsbVs4gxDyUxAexRS32-5iNYZw-4htqxBUWV0CAk6wJOkfC9dlwKW-1NjBYUQtw9GGpooCRRZ5UWU96KxmGQm0eMWzqc9YZb2WOn9HrFA/s1600/Euro_coins_and_banknotes.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhB9W4V6rrzZ0CHVVUd72e5jPhsmQ4KtZsbVs4gxDyUxAexRS32-5iNYZw-4htqxBUWV0CAk6wJOkfC9dlwKW-1NjBYUQtw9GGpooCRRZ5UWU96KxmGQm0eMWzqc9YZb2WOn9HrFA/s1600/Euro_coins_and_banknotes.jpg" height="150" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">To change the team means to earn more<br />money...what about the football millionaires?<br />How often do they change the team?</td></tr>
</tbody></table>
Are you ready for more statistics on your favorite kind of sports? Well, data is fun, and obviously Big Data means Big Fun. There are lot's of interesting things to discover while exploring data, and wikipedia (i.e. dbpedia for the insiders) provides all the necessary means.
<br />
<br />
Have you ever wondered about this kind of <b>slave trade in professional football?</b> Well, I wouldn't exactly call the transfer of a millionaire to a higher paying job a 'slave trade'. But, have you ever thought about the following question: Do the real good (and well paid) players more often change the team - or is it vice versa, that teams try to get rid of players that have a bad season or are on the decline? Who knows? Let's have a look on the data:
<br />
<table border="1" class="sparql">
<tbody>
<tr>
<th>TeamChanges</th>
<th>NumPlayers</th>
</tr>
<tr>
<td>16</td>
<td>2</td>
</tr>
<tr>
<td>15</td>
<td>26</td>
</tr>
<tr>
<td>14</td>
<td>84</td>
</tr>
<tr>
<td>13</td>
<td>287</td>
</tr>
<tr>
<td>12</td>
<td>792</td>
</tr>
<tr>
<td>11</td>
<td>2247</td>
</tr>
<tr>
<td>10</td>
<td>3848</td>
</tr>
<tr>
<td>9</td>
<td>5109</td>
</tr>
<tr>
<td>8</td>
<td>6464</td>
</tr>
<tr>
<td>7</td>
<td>8110</td>
</tr>
<tr>
<td>6</td>
<td>9790</td>
</tr>
<tr>
<td>5</td>
<td>11264</td>
</tr>
<tr>
<td>4</td>
<td>11837</td>
</tr>
<tr>
<td>3</td>
<td>11448</td>
</tr>
<tr>
<td>2</td>
<td>10961</td>
</tr>
<tr>
<td>1</td>
<td>6515</td>
</tr>
</tbody></table>
Here, we have a table providing an overview about how many players (in wikipedia) have changed their team for how many times [1]. Obviously, it seems to be some kind of Gaussian distribution with a peak between 2 and 6 team switches. OK, what about the players? Where are the top players listed in this table? Well, David Beckham switched team 11 times according to wikipedia, Cristiano Ronaldo 8 times, Thierry Henry 9 times. At least these numbers are above average which we had identified to be between 2 and 6. This seems to give proof to our original assumption.<br />
<br />
<table border="1" class="sparql">
<tbody>
<tr>
<th>person</th>
<th>TeamChanges</th>
<th>popularity</th>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Cristiano_Ronaldo">http://dbpedia.org/resource/Cristiano_Ronaldo</a></td>
<td>8</td>
<td>1794</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/David_Beckham">http://dbpedia.org/resource/David_Beckham</a></td>
<td>11</td>
<td>1572</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Thierry_Henry">http://dbpedia.org/resource/Thierry_Henry</a></td>
<td>9</td>
<td>1414</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Lionel_Messi">http://dbpedia.org/resource/Lionel_Messi</a></td>
<td>7</td>
<td>1404</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Wayne_Rooney">http://dbpedia.org/resource/Wayne_Rooney</a></td>
<td>5</td>
<td>1343</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Frank_Lampard">http://dbpedia.org/resource/Frank_Lampard</a></td>
<td>5</td>
<td>1188</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Pel%C3%A9">http://dbpedia.org/resource/Pel%C3%A9</a></td>
<td>4</td>
<td>1111</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Didier_Drogba">http://dbpedia.org/resource/Didier_Drogba</a></td>
<td>8</td>
<td>1047</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Ronaldo">http://dbpedia.org/resource/Ronaldo</a></td>
<td>10</td>
<td>1037</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/resource/Michael_Owen">http://dbpedia.org/resource/Michael_Owen</a></td>
<td>6</td>
<td>1011</td>
</tr>
</tbody></table>
<br />
But, we get a better overview, if we look at the average popularity of each switching group in the table [3]:<br />
<div>
<table border="1" class="sparql">
<tbody>
<tr>
<th>TeamChanges</th>
<th>NumPlayers</th>
<th>avgindegree</th>
</tr>
<tr>
<td>16</td>
<td>2</td>
<td>107.5</td>
</tr>
<tr>
<td>15</td>
<td>26</td>
<td>76.769230769230769</td>
</tr>
<tr>
<td>14</td>
<td>84</td>
<td>47.297619047619048</td>
</tr>
<tr>
<td>13</td>
<td>287</td>
<td>60.885017421602787</td>
</tr>
<tr>
<td>12</td>
<td>788</td>
<td>45.073604060913706</td>
</tr>
<tr>
<td>11</td>
<td>2235</td>
<td>37.206263982102908</td>
</tr>
<tr>
<td>10</td>
<td>3800</td>
<td>32.577631578947368</td>
</tr>
<tr>
<td>9</td>
<td>5023</td>
<td>30.939080230937687</td>
</tr>
<tr>
<td>8</td>
<td>6332</td>
<td>29.685881238155401</td>
</tr>
<tr>
<td>7</td>
<td>7935</td>
<td>25.499054820415879</td>
</tr>
<tr>
<td>6</td>
<td>9525</td>
<td>23.188346456692913</td>
</tr>
<tr>
<td>5</td>
<td>10886</td>
<td>18.682895462061363</td>
</tr>
<tr>
<td>4</td>
<td>11423</td>
<td>14.937844699290904</td>
</tr>
<tr>
<td>3</td>
<td>10842</td>
<td>11.131802250507286</td>
</tr>
<tr>
<td>2</td>
<td>10089</td>
<td>7.704628803647537</td>
</tr>
<tr>
<td>1</td>
<td>5534</td>
<td>5.19588001445609</td>
</tr>
</tbody></table>
As we had originally thought, on the average, the popularity of the players rises with the number of team changes. Although the top group with 16 changes is far from the highest possible popularity scores (as e.g. 1572 for David Beckham). Hmm, maybe there is a correlation among the number of achieved goals with the number of team changes? Is it more likely that a top goal hunter switches team more often? Let's have a look [4]:<br />
<table border="1" class="sparql">
<tbody>
<tr>
<th>TeamChanges</th>
<th>NumPlayers</th>
<th>AvgGoals</th>
</tr>
<tr>
<td>16</td>
<td>2</td>
<td>22.5</td>
</tr>
<tr>
<td>15</td>
<td>26</td>
<td>28.807692307692308</td>
</tr>
<tr>
<td>14</td>
<td>83</td>
<td>37.855421686746988</td>
</tr>
<tr>
<td>13</td>
<td>287</td>
<td>38.062717770034843</td>
</tr>
<tr>
<td>12</td>
<td>781</td>
<td>39.939820742637644</td>
</tr>
<tr>
<td>11</td>
<td>2205</td>
<td>38.625850340136054</td>
</tr>
<tr>
<td>10</td>
<td>3784</td>
<td>35.646141649048626</td>
</tr>
<tr>
<td>9</td>
<td>4980</td>
<td>32.826907630522088</td>
</tr>
<tr>
<td>8</td>
<td>6296</td>
<td>29.489517153748412</td>
</tr>
<tr>
<td>7</td>
<td>7826</td>
<td>24.455788397648863</td>
</tr>
<tr>
<td>6</td>
<td>9314</td>
<td>21.937835516426884</td>
</tr>
<tr>
<td>5</td>
<td>10471</td>
<td>18.655142775284118</td>
</tr>
<tr>
<td>4</td>
<td>10627</td>
<td>16.317869577491296</td>
</tr>
<tr>
<td>3</td>
<td>9612</td>
<td>12.756450270495214</td>
</tr>
<tr>
<td>2</td>
<td>7193</td>
<td>9.911858751564021</td>
</tr>
<tr>
<td>1</td>
<td>4624</td>
<td>4.444204152249135</td>
</tr>
</tbody></table>
<br />
Looks interesting. Top goal scorer have 9 to 14 team switches. This is way above the average. Thus, the more goals you score, the more often you will have the chance of being transferred (and thus earn more money). Players that don't score goals will obviously not be transferred (that often).</div>
<div>
<br /></div>
<div>
<b>References:</b></div>
<div>
[1] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0ASELECT+%3Fgteam+as+%3FTeamChanges+COUNT%28*%29+as+%3FNumPlayers+WHERE%0D%0A%7B%0D%0ASELECT+DISTINCT+%3Fperson+COUNT%28DISTINCT+%3Fteam%29+as+%3Fgteam+WHERE%0D%0A%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+%3B%0D%0A+++++++++dbpedia-owl%3Ateam+%3Fteam+.%0D%0A%7D%0D%0AGROUP+by+%3Fperson%0D%0A%7D%0D%0AGROUP+BY+%3Fgteam%0D%0AORDER+BY+DESC+%28%3Fgteam%29&format=text%2Fhtml&timeout=30000&debug=on">How many players have how many team changes overall?</a><br />
[2] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0ASELECT+%3Fgteam+as+%3FTeamChanges+COUNT%28*%29+as+%3FNumPlayers+WHERE%0D%0A%7B%0D%0ASELECT+DISTINCT+%3Fperson+COUNT%28DISTINCT+%3Fteam%29+as+%3Fgteam+WHERE%0D%0A%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+%3B%0D%0A+++++++++dbpedia-owl%3Ateam+%3Fteam+.%0D%0A%7D%0D%0AGROUP+by+%3Fperson%0D%0A%7D%0D%0AGROUP+BY+%3Fgteam%0D%0AORDER+BY+DESC+%28%3Fgteam%29&format=text%2Fhtml&timeout=30000&debug=on">The Top10 popular soccer players and their number of team changes</a><br />
[3] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0ASELECT+%3Fgteam+as+%3FTeamChanges+COUNT%28*%29+as+%3FNumPlayers+WHERE%0D%0A%7B%0D%0ASELECT+DISTINCT+%3Fperson+COUNT%28DISTINCT+%3Fteam%29+as+%3Fgteam+WHERE%0D%0A%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+%3B%0D%0A+++++++++dbpedia-owl%3Ateam+%3Fteam+.%0D%0A%7D%0D%0AGROUP+by+%3Fperson%0D%0A%7D%0D%0AGROUP+BY+%3Fgteam%0D%0AORDER+BY+DESC+%28%3Fgteam%29&format=text%2Fhtml&timeout=30000&debug=on">The average popularity of football players regarding the number of team switches</a><br />
[4] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0ASELECT+%3Fgteam+as+%3FTeamChanges+COUNT+%28DISTINCT+%3Fperson%29+as+%3FNumPlayers+AVG%28%3Fsumgoals%29+as+%3FAvgGoals+WHERE%0D%0A%7B%0D%0A%0D%0A%7B%0D%0ASELECT+DISTINCT+%3Fperson+COUNT%28DISTINCT+%3Fteam%29+as+%3Fgteam+WHERE%0D%0A%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+%3B%0D%0A+++++++++dbpedia-owl%3Ateam+%3Fteam+.%0D%0A%7D%0D%0AGROUP+by+%3Fperson%0D%0A%7D%0D%0A%0D%0A%7B+SELECT+%3Fperson+SUM%28%3Fgoals%29+as+%3Fsumgoals+WHERE+%7B%0D%0A++%3Fperson+dbpprop%3Agoals+%3Fgoals+FILTER+%28isNUMERIC%28%3Fgoals%29%29+FILTER+%28%3Fgoals+%3C+500+%29.%0D%0A+%7D+GROUP+BY+%3Fperson%0D%0A%7D%0D%0A%0D%0A%7D%0D%0AGROUP+BY+%3Fgteam%0D%0AORDER+BY+DESC%28%3Fgteam%29%0D%0A&format=text%2Fhtml&timeout=30000&debug=on">Number of average goals per soccer player with respect to the number of team changes</a></div>
Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comPotsdam, Germany52.3905689 13.06447290000005552.2356559 12.741749400000055 52.5454819 13.387196400000056tag:blogger.com,1999:blog-17174674.post-6930891611728089002014-06-25T12:02:00.002+02:002014-06-25T15:06:05.156+02:00Harald's Original Miscellany - The Truth about Football - Part 2<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: justify;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjd6qW9hwyujpAGN5TYQ6wE1dH-uoBVP-2dmaZQIXXi63ESO6kRVPoibdhZWKmJgdmGb10Drpszv1MJqOEAMa07cUC3ARPEmOT8RGWvuXrB4XeBrZ26UB619U0sodp_cSJHchnw2w/s1600/JohnTerry-moon_2225332a.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjd6qW9hwyujpAGN5TYQ6wE1dH-uoBVP-2dmaZQIXXi63ESO6kRVPoibdhZWKmJgdmGb10Drpszv1MJqOEAMa07cUC3ARPEmOT8RGWvuXrB4XeBrZ26UB619U0sodp_cSJHchnw2w/s1600/JohnTerry-moon_2225332a.jpg" height="204" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">John Terry Celebration Meme, read on and you will understand...</td></tr>
</tbody></table>
<div style="text-align: justify;">
Of course you always wanted to know, who is the best football player of all times. Sure this might be a question about which real football afficionados might argue forever. Also Wikipedia will not be able to give you the definite answer. But, we can play around with the available data and maybe we find out something interesting about football players again ...</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
But, first at all, I want to say thank you to <a href="https://twitter.com/kidehen">Kingsley Idehen</a>, who gave me the hint for my SPARQL query links to use the parameter "<span style="font-family: Courier New, Courier, monospace;">qtxt=</span>" instead of "<span style="font-family: Courier New, Courier, monospace;">query=</span>", which enables others to see the original query and to use it for further data explorations. Thus, all SPARQL query links will be given in this form.
</div>
<div style="text-align: justify;">
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<div style="text-align: justify;">
So let's start with the most simple query: Select all football players and their popularity (indegree) in descending order starting with the most popular player. We must be a little bit careful, because the class <span style="font-family: Courier New, Courier, monospace;">SoccerPlayer </span>does not only contain "real persons" but also popular roles of football players such as e.g. "Captain". Therefore, we filter the results for entities that have a name (via <span style="font-family: Courier New, Courier, monospace;">foaf:name</span>). Here are the <b>Top50 football players according to wikipedia</b>. For the entire list, please refer to the references [1].</div>
<table border="1" class="sparql" style="text-align: justify;">
<tbody>
<tr>
<th>Name</th>
<th>Popularity</th>
</tr>
<tr>
<td>Cristiano Ronaldo</td>
<td>1794</td>
</tr>
<tr>
<td>David Beckham</td>
<td>1572</td>
</tr>
<tr>
<td>Thierry Henry</td>
<td>1414</td>
</tr>
<tr>
<td>Lionel Messi</td>
<td>1404</td>
</tr>
<tr>
<td>Wayne Rooney</td>
<td>1343</td>
</tr>
<tr>
<td>Frank Lampard</td>
<td>1188</td>
</tr>
<tr>
<td>Pelé</td>
<td>1111</td>
</tr>
<tr>
<td>Didier Drogba</td>
<td>1047</td>
</tr>
<tr>
<td>Ronaldo</td>
<td>1037</td>
</tr>
<tr>
<td>Michael Owen</td>
<td>1011</td>
</tr>
<tr>
<td>Steven Gerrard</td>
<td>1002</td>
</tr>
<tr>
<td>Zlatan Ibrahimović</td>
<td>964</td>
</tr>
<tr>
<td>Alessandro Del Piero</td>
<td>926</td>
</tr>
<tr>
<td>Ronaldinho</td>
<td>914</td>
</tr>
<tr>
<td>Raúl (footballer)</td>
<td>903</td>
</tr>
<tr>
<td>Ryan Giggs</td>
<td>894</td>
</tr>
<tr>
<td>Fernando Torres</td>
<td>889</td>
</tr>
<tr>
<td>Zinedine Zidane</td>
<td>867</td>
</tr>
<tr>
<td>Ruud van Nistelrooy</td>
<td>861</td>
</tr>
<tr>
<td>Robbie Keane</td>
<td>861</td>
</tr>
<tr>
<td>Samuel Eto'o</td>
<td>859</td>
</tr>
<tr>
<td>Landon Donovan</td>
<td>835</td>
</tr>
<tr>
<td>Andriy Shevchenko</td>
<td>823</td>
</tr>
<tr>
<td>Kaká</td>
<td>804</td>
</tr>
<tr>
<td>Francesco Totti</td>
<td>730</td>
</tr>
<tr>
<td>Robin van Persie</td>
<td>720</td>
</tr>
<tr>
<td>Paul Scholes</td>
<td>692</td>
</tr>
<tr>
<td>Hernán Crespo</td>
<td>680</td>
</tr>
<tr>
<td>David Villa</td>
<td>669</td>
</tr>
<tr>
<td>John Terry</td>
<td>669</td>
</tr>
<tr>
<td>Cesc Fàbregas</td>
<td>669</td>
</tr>
<tr>
<td>George Best</td>
<td>667</td>
</tr>
<tr>
<td>Carlos Tévez</td>
<td>666</td>
</tr>
<tr>
<td>Robinho</td>
<td>643</td>
</tr>
<tr>
<td>Gary Lineker</td>
<td>641</td>
</tr>
<tr>
<td>Teddy Sheringham</td>
<td>633</td>
</tr>
<tr>
<td>Andrew Cole</td>
<td>620</td>
</tr>
<tr>
<td>Dwayne De Rosario</td>
<td>617</td>
</tr>
<tr>
<td>Xavi</td>
<td>616</td>
</tr>
<tr>
<td>Jermain Defoe</td>
<td>613</td>
</tr>
<tr>
<td>Craig Bellamy</td>
<td>609</td>
</tr>
<tr>
<td>Dimitar Berbatov</td>
<td>587</td>
</tr>
<tr>
<td>David Trezeguet</td>
<td>587</td>
</tr>
<tr>
<td>Luis Suárez</td>
<td>581</td>
</tr>
<tr>
<td>Peter Crouch</td>
<td>577</td>
</tr>
<tr>
<td>Michael Ballack</td>
<td>572</td>
</tr>
<tr>
<td>Miroslav Klose</td>
<td>568</td>
</tr>
<tr>
<td>Luís Figo</td>
<td>567</td>
</tr>
<tr>
<td>Lee Dong-Gook</td>
<td>558</td>
</tr>
<tr>
<td>Filippo Inzaghi</td>
<td>557</td>
</tr>
</tbody></table>
<div style="text-align: justify;">
Yes, it was obvious for everybody that names such as Ronaldo, Beckham, Thierry, Pelé occur among the top popular players. Unfortunately, I'm not a football expert to comment further on that. Let's have a look, whether popularity corresponds with the number of achieved goals. However, this information is not easy to extract. For some of the football players, there's a property <span style="font-family: Courier New, Courier, monospace;">dbprop:totalGoals</span>, while most of them has <span style="font-family: Courier New, Courier, monospace;">dbprop:goals</span>. But the later sometimes exists multiple times for single years or periods. Thus, we have to sum up all dbprop:goals, while keeping in mind not to count any number more often than once (because an entry might be reproduced in our result list for several reasons).</div>
<table border="1" class="sparql" style="text-align: justify;">
<tbody>
<tr>
<th>Name</th>
<th>Goals</th>
<th>Popularity</th>
</tr>
<tr>
<td>David Schofield (footballer)</td>
<td>76543210</td>
<td>9</td>
</tr>
<tr>
<td>Alcindo Sartori</td>
<td>5019110</td>
<td>160</td>
</tr>
<tr>
<td>Oh Seung-Bum</td>
<td>1842256</td>
<td>32</td>
</tr>
<tr>
<td>Marei Al Ramly</td>
<td>6037</td>
<td>11</td>
</tr>
<tr>
<td>Darío Espínola</td>
<td>1715</td>
<td>6</td>
</tr>
<tr>
<td>Kim Andersson</td>
<td>1537</td>
<td>23</td>
</tr>
<tr>
<td>Stefan Lövgren</td>
<td>1328</td>
<td>18</td>
</tr>
<tr>
<td>Nikola Karabatić</td>
<td>1318</td>
<td>84</td>
</tr>
<tr>
<td>Elias Ribeiro de Oliveira</td>
<td>1187</td>
<td>26</td>
</tr>
<tr>
<td>Mohd Amar Rohidan</td>
<td>1020</td>
<td>38</td>
</tr>
<tr>
<td>Slaviša Žungul</td>
<td>856</td>
<td>113</td>
</tr>
<tr>
<td>John Bartley (footballer)</td>
<td>762</td>
<td>1</td>
</tr>
<tr>
<td>Zoran Karić</td>
<td>759</td>
<td>11</td>
</tr>
<tr>
<td>Jimmy Greaves</td>
<td>748</td>
<td>342</td>
</tr>
<tr>
<td>Ernest Spiteri Gonzi</td>
<td>704</td>
<td>11</td>
</tr>
<tr>
<td>Pierre van Hooijdonk</td>
<td>670</td>
<td>238</td>
</tr>
<tr>
<td>Reg Date</td>
<td>664</td>
<td>3</td>
</tr>
<tr>
<td>Trevor Phillips (footballer)</td>
<td>655</td>
<td>4</td>
</tr>
<tr>
<td>Joan Linares</td>
<td>645</td>
<td>12</td>
</tr>
<tr>
<td>Domenic Mobilio</td>
<td>625</td>
<td>58</td>
</tr>
<tr>
<td>Pelé</td>
<td>620</td>
<td>1111</td>
</tr>
<tr>
<td>Harry Johnson (footballer born 1899)</td>
<td>610</td>
<td>25</td>
</tr>
<tr>
<td>Ernst Stojaspal</td>
<td>602</td>
<td>23</td>
</tr>
<tr>
<td>Max Morlock</td>
<td>588</td>
<td>66</td>
</tr>
<tr>
<td>Ernie Hine</td>
<td>574</td>
<td>72</td>
</tr>
<tr>
<td>Branko Šegota</td>
<td>561</td>
<td>38</td>
</tr>
<tr>
<td>Serhiy Koridze</td>
<td>557</td>
<td>4</td>
</tr>
<tr>
<td>Salvinu Schembri</td>
<td>538</td>
<td>6</td>
</tr>
<tr>
<td>Konstantin Yeryomenko</td>
<td>537</td>
<td>13</td>
</tr>
<tr>
<td>Tony Brown (English footballer)</td>
<td>498</td>
<td>47</td>
</tr>
<tr>
<td>Waldo Machado</td>
<td>497</td>
<td>36</td>
</tr>
<tr>
<td>Nguyen Minh Phuong</td>
<td>496</td>
<td>50</td>
</tr>
<tr>
<td>Tony Cascarino</td>
<td>496</td>
<td>141</td>
</tr>
<tr>
<td>Leônidas da Silva</td>
<td>484</td>
<td>98</td>
</tr>
<tr>
<td>Zeki Rıza Sporel</td>
<td>470</td>
<td>75</td>
</tr>
<tr>
<td>Ángeles Parejo</td>
<td>469</td>
<td>9</td>
</tr>
<tr>
<td>Tommy Dickson</td>
<td>457</td>
<td>13</td>
</tr>
<tr>
<td>Elisabetta Vignotto</td>
<td>454</td>
<td>22</td>
</tr>
<tr>
<td>Alberto Spencer</td>
<td>445</td>
<td>99</td>
</tr>
<tr>
<td>Stefan Schwoch</td>
<td>435</td>
<td>7</td>
</tr>
<tr>
<td>Peter Kitchen</td>
<td>429</td>
<td>16</td>
</tr>
<tr>
<td>Edgar Kail</td>
<td>427</td>
<td>7</td>
</tr>
<tr>
<td>Eusébio</td>
<td>423</td>
<td>433</td>
</tr>
<tr>
<td>Giorgos Sideris</td>
<td>415</td>
<td>51</td>
</tr>
<tr>
<td>Tommy Browell</td>
<td>414</td>
<td>88</td>
</tr>
<tr>
<td>Patricio Margetic</td>
<td>412</td>
<td>14</td>
</tr>
<tr>
<td>Arsénio Trindade Duarte</td>
<td>409</td>
<td>19</td>
</tr>
<tr>
<td>Uwe Seeler</td>
<td>406</td>
<td>153</td>
</tr>
<tr>
<td>Hughie Gallacher</td>
<td>406</td>
<td>131</td>
</tr>
<tr>
<td>Dragan Džajić</td>
<td>401</td>
<td>150</td>
</tr>
</tbody></table>
<div style="text-align: justify;">
Again we see, that DBpedia data (resp. Wikipedia data) is somehow 'noisy'. The first 3 ranks are obviously wrong concerning the number of goals. Simply because if <b>David Schofield</b> really would have achieved 76,543,210 goals, it would mean that he had won about 5 goals per minute of all the 32 years of his entire life so far. This must be kind of an extraction error. If we look at the players with more than 1000 goals, then a closer inspection reveals some handballers that either are also footballers or are wrongly declared to be footballers. In handball it is easier to achieve a higher number of goals compared to football. <b>Trevor Phillips</b> and <b>John Bartley</b> really achieved more than 600 goals, but their popularity score signals that they did achieve this not necessarely in the major league. The first top ranked prominent football player in this list definitely is<b> Pelé</b> with 620 goals. The only other two in this Top50 list I have already heard of are <b>Eusébio</b> and <b>Uwe Seeler</b>, but don't take me as a reference :)</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
Lets order the list again the other way around according to the most popular players to investigate their goal score:</div>
<table border="1" class="sparql" style="text-align: justify;">
<tbody>
<tr>
<th>Name</th>
<th>Goals</th>
<th>Popularity</th>
</tr>
<tr>
<td>Cristiano Ronaldo</td>
<td>227</td>
<td>1794</td>
</tr>
<tr>
<td>David Beckham</td>
<td>95</td>
<td>1572</td>
</tr>
<tr>
<td>Thierry Henry</td>
<td>265</td>
<td>1414</td>
</tr>
<tr>
<td>Lionel Messi</td>
<td>223</td>
<td>1404</td>
</tr>
<tr>
<td>Wayne Rooney</td>
<td>156</td>
<td>1343</td>
</tr>
<tr>
<td>Frank Lampard</td>
<td>163</td>
<td>1188</td>
</tr>
<tr>
<td>Pelé</td>
<td>620</td>
<td>1111</td>
</tr>
<tr>
<td>Didier Drogba</td>
<td>160</td>
<td>1047</td>
</tr>
<tr>
<td>Ronaldo</td>
<td>217</td>
<td>1037</td>
</tr>
<tr>
<td>Michael Owen</td>
<td>163</td>
<td>1011</td>
</tr>
<tr>
<td>Steven Gerrard</td>
<td>98</td>
<td>1002</td>
</tr>
<tr>
<td>Zlatan Ibrahimović</td>
<td>198</td>
<td>964</td>
</tr>
<tr>
<td>Alessandro Del Piero</td>
<td>223</td>
<td>926</td>
</tr>
<tr>
<td>Ronaldinho</td>
<td>157</td>
<td>914</td>
</tr>
<tr>
<td>Raúl (footballer)</td>
<td>280</td>
<td>903</td>
</tr>
<tr>
<td>Ryan Giggs</td>
<td>114</td>
<td>894</td>
</tr>
<tr>
<td>Fernando Torres</td>
<td>161</td>
<td>889</td>
</tr>
<tr>
<td>Zinedine Zidane</td>
<td>95</td>
<td>867</td>
</tr>
<tr>
<td>Ruud van Nistelrooy</td>
<td>249</td>
<td>861</td>
</tr>
<tr>
<td>Robbie Keane</td>
<td>179</td>
<td>861</td>
</tr>
<tr>
<td>Samuel Eto'o</td>
<td>219</td>
<td>859</td>
</tr>
<tr>
<td>Landon Donovan</td>
<td>135</td>
<td>835</td>
</tr>
<tr>
<td>Andriy Shevchenko</td>
<td>219</td>
<td>823</td>
</tr>
<tr>
<td>Kaká</td>
<td>114</td>
<td>804</td>
</tr>
<tr>
<td>Francesco Totti</td>
<td>226</td>
<td>730</td>
</tr>
<tr>
<td>Robin van Persie</td>
<td>130</td>
<td>720</td>
</tr>
<tr>
<td>Paul Scholes</td>
<td>107</td>
<td>692</td>
</tr>
<tr>
<td>Hernán Crespo</td>
<td>198</td>
<td>680</td>
</tr>
<tr>
<td><b>John Terry</b></td>
<td><b>30</b></td>
<td><b>669</b></td>
</tr>
<tr>
<td>David Villa</td>
<td>234</td>
<td>669</td>
</tr>
<tr>
<td>Cesc Fàbregas</td>
<td>50</td>
<td>669</td>
</tr>
<tr>
<td>George Best</td>
<td>238</td>
<td>667</td>
</tr>
<tr>
<td>Carlos Tévez</td>
<td>135</td>
<td>666</td>
</tr>
<tr>
<td>Robinho</td>
<td>122</td>
<td>643</td>
</tr>
<tr>
<td>Gary Lineker</td>
<td>243</td>
<td>641</td>
</tr>
<tr>
<td>Teddy Sheringham</td>
<td>289</td>
<td>633</td>
</tr>
<tr>
<td>Andrew Cole</td>
<td>226</td>
<td>620</td>
</tr>
<tr>
<td>Dwayne De Rosario</td>
<td>94</td>
<td>617</td>
</tr>
<tr>
<td>Xavi</td>
<td>57</td>
<td>616</td>
</tr>
<tr>
<td>Jermain Defoe</td>
<td>151</td>
<td>613</td>
</tr>
<tr>
<td>Craig Bellamy</td>
<td>113</td>
<td>609</td>
</tr>
<tr>
<td>Dimitar Berbatov</td>
<td>189</td>
<td>587</td>
</tr>
<tr>
<td>David Trezeguet</td>
<td>218</td>
<td>587</td>
</tr>
<tr>
<td>Luis Suárez</td>
<td>128</td>
<td>581</td>
</tr>
<tr>
<td>Peter Crouch</td>
<td>102</td>
<td>577</td>
</tr>
<tr>
<td>Michael Ballack</td>
<td>117</td>
<td>572</td>
</tr>
<tr>
<td>Miroslav Klose</td>
<td>181</td>
<td>568</td>
</tr>
<tr>
<td>Luís Figo</td>
<td>91</td>
<td>567</td>
</tr>
<tr>
<td>Filippo Inzaghi</td>
<td>184</td>
<td>557</td>
</tr>
<tr>
<td>Patrick Vieira</td>
<td>45</td>
<td>551</td>
</tr>
</tbody></table>
<div style="text-align: justify;">
As we would expect, most of the popular football players are also good goal scorers. Well, there are a few exceptions. Take <b>John Terry </b>with a popularity score of 669 and only 30 goals. Why might he be so popular then? Taking a closer look at Wikipedia reveals that Terry plays at centre back position and is the captain of Chelsea in the Premier League. Well, that's already something for popularity. But, if you look even closer, you will find more: under the topic 'Controversies' you will find charges for assault and affray, a £60 fine for parking his Bentley in a disabled bay, extramarital affair allegations as well as racial abuse allegations. But, neither of these is directly responsible for Terry's popularity. In fact it's an internet meme (cf. introductory picture of this article).
John Terry was suspended for the UEFA Final and had to watch his team in a suit and tie on the sidelines. He did look quite miserable as he sat there, watching his team defend for their lives and then miraculously pull out the victory. However, as soon as Chelsea made the victory, it was <b>party time for Terry</b>! He immediately threw off his suit like Superman and revealed his full Chelsea kit underneath his suit. The internet community enjoyed his dedication to his club and soccer so much that immediately a popular internet meme lampooning his behaviour appeared on the web, becoming one of the most popular online jokes in 2012. Terry has been pictured taking part in great moments in history and fiction. These included the fall of the Berlin Wall, the freeing of Nelson Mandela, the triumph of Rocky Balboa, as well as the first landing on the Moon [3]. Well, this should be reason for some popularity :)
</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
[1] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0A%0D%0ASELECT+DISTINCT+STR%28%3Fname%29+%3Findegree%0D%0AWHERE+%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+.%0D%0A+%3Fperson+dbpedia-owl%3AwikiPageInLinkCount+%3Findegree+.%0D%0A+%3Fperson+foaf%3Aname+%3Fbirthname+.%0D%0A+%3Fperson+rdfs%3Alabel+%3Fname+FILTER%28LANG%28%3Fname%29+%3D+%22en%22%29+.%0D%0A+%0D%0A%7D%0D%0AORDER+by+DESC%28%3Findegree%29%0D%0A&format=text%2Fhtml&timeout=30000&debug=on">List of most popular Football Players</a></div>
<div style="text-align: justify;">
[2] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&qtxt=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E%0D%0APREFIX+dbprop%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2F%3E%0D%0A%0D%0ASELECT+DISTINCT+STR%28%3Fname%29+as+%3FName+SUM%28xsd%3Ainteger%28%3Fgoals%29%29+as+%3FGoals+AVG%28%3Findegree%29+as+%3FPopularity%0D%0AWHERE+%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+.%0D%0A+%3Fperson+dbpedia-owl%3AwikiPageInLinkCount+%3Findegree+.%0D%0A+%3Fperson+dbprop%3Afullname+%3Fbirthname+.%0D%0A+OPTIONAL+%7B+%3Fperson+dbpprop%3Agoals+%3Fgoals+.+%7D%0D%0A+%3Fperson+rdfs%3Alabel+%3Fname+FILTER%28LANG%28%3Fname%29+%3D+%22en%22%29+.%0D%0A+%0D%0A%7D%0D%0AGROUP+BY+%3Fname%0D%0AORDER+by+DESC%28%3FGoals%29%0D%0ALIMIT+50&format=text%2Fhtml&timeout=30000&debug=on">List of top goal scorer among popular Football players</a></div>
<div style="text-align: justify;">
[3] <a href="http://runninwithit.com/john-terry-celebration-memes/">John Terry's Celebration Memes</a></div>
Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-59173455841999077162014-06-23T13:01:00.001+02:002014-06-23T13:18:57.690+02:00Harald's Original Miscellany - The Truth about Football<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhehgCmcnLhg-MilufDj6s_jBBIu1fbu9vwfpdDWwfjsAwdIJreyiZ0MM3H-UXFVTb1p8WltZ9HqMFWS_I9TI0zAvBER_xauasQCOndPUaMtK1YSxTkTkO-wloDY6HaAcnmQw-03Q/s1600/England_1893.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhehgCmcnLhg-MilufDj6s_jBBIu1fbu9vwfpdDWwfjsAwdIJreyiZ0MM3H-UXFVTb1p8WltZ9HqMFWS_I9TI0zAvBER_xauasQCOndPUaMtK1YSxTkTkO-wloDY6HaAcnmQw-03Q/s1600/England_1893.jpg" height="234" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">The England National football team, 1893, photo: wikipedia</td></tr>
</tbody></table>
Well, it's the time of the <b>Worldcup 2014</b>. Why should I bother you with the peculiarities of authors and writers, when we can also have a look on Football! As you might remember, we had about <b>15,328 </b>individuals in Wikipedia classified as authors [1]. What do you think, how many footballers are there compared to authors? Well there's a huge difference:<b> 162,597 referenced footballer players</b>, i.e. about 10 times as much as authors [2]. Maybe you think now there might be an overlap. How many football players are also listed as authors? I am sorry to disappoint you, but there is no overlap. No football player is also listed as being an author.<br />
<br />
<b>Fact No. 1: Footballers are no authors, and vice versa.</b><br />
<br />
So you might wonder, what other categories these football players are in to get a better overview about what we are talking about. Interestingly, when looking at the most popular categories, you will soon find the large number of expatriates among those players. The <b>Top5 expatriate nationalities</b> among football players are: Brazil, Argentina, Russia, France, Serbia [3]. If you look at the bottom of the list, you will find the more or less exotic combinations, such as e.g. Hungarian expatriates in Uzbekistan, or Cameroonian expatriates in Venezuela, or even German expatriates in the Netherlands ;-)<br />
<br />
<b>Fact No. 2: Only a few Hungarian football players emigrate to Uzbekistan.</b><br />
<br />
And in which countries they prefer to emigrate? The <b>Top5 countries for football players to emigrate </b>are: England, Germany, Spain, Italy, France [4]. At least for France the statistics seems to be balanced somehow, while England and Germany are the leading nations to attract foreign football players all around the world. Very interesting also the bottom of the list, where as the "least attractive" countries Gambia, Guam, Nepal, or Antigua and Barbuda are listed.<br />
<br />
<b>Fact No.3: French football players seem to be undecided whether to stay or leave the country.</b><br />
<br />
But what about the categories that do not have a direct relationship to football in the first place? Let's filter out these categories and let's have a look on what football players are up to.<br />
<br />
<table border="1" class="sparql">
<tbody>
<tr>
<th>alternative professions of football players</th>
<th>#players</th>
</tr>
<tr><td><a href="http://dbpedia.org/class/yago/Director110014939">http://dbpedia.org/class/yago/Director110014939</a></td>
<td>279</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Migrant110314952">http://dbpedia.org/class/yago/Migrant110314952</a></td>
<td>271</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/National109625401">http://dbpedia.org/class/yago/National109625401</a></td>
<td>250</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Intellectual109621545">http://dbpedia.org/class/yago/Intellectual109621545</a></td>
<td>237</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Alumnus109786338">http://dbpedia.org/class/yago/Alumnus109786338</a></td>
<td>234</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Scholar110557854">http://dbpedia.org/class/yago/Scholar110557854</a></td>
<td>234</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Striker110663996">http://dbpedia.org/class/yago/Striker110663996</a></td>
<td>134</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Worker109632518">http://dbpedia.org/class/yago/Worker109632518</a></td>
<td>73</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/SkilledWorker110605985">http://dbpedia.org/class/yago/SkilledWorker110605985</a></td>
<td>64</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Unfortunate109630641">http://dbpedia.org/class/yago/Unfortunate109630641</a></td>
<td>56</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Trainer110722575">http://dbpedia.org/class/yago/Trainer110722575</a></td>
<td>55</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Coach109931640">http://dbpedia.org/class/yago/Coach109931640</a></td>
<td>54</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Communicator109610660">http://dbpedia.org/class/yago/Communicator109610660</a></td>
<td>51</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Serviceman110582746">http://dbpedia.org/class/yago/Serviceman110582746</a></td>
<td>51</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Expert109617867">http://dbpedia.org/class/yago/Expert109617867</a></td>
<td>43</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Observer110369528">http://dbpedia.org/class/yago/Observer110369528</a></td>
<td>33</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Cricketer109977326">http://dbpedia.org/class/yago/Cricketer109977326</a></td>
<td>32</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Adult109605289">http://dbpedia.org/class/yago/Adult109605289</a></td>
<td>28</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Victim110752093">http://dbpedia.org/class/yago/Victim110752093</a></td>
<td>28</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Rival110533013">http://dbpedia.org/class/yago/Rival110533013</a></td>
<td>27</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Official110372076">http://dbpedia.org/class/yago/Official110372076</a></td>
<td>26</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Adjudicator109769636">http://dbpedia.org/class/yago/Adjudicator109769636</a></td>
<td>26</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/MilitaryOfficer110317007">http://dbpedia.org/class/yago/MilitaryOfficer110317007</a></td>
<td>25</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/EnlistedPerson110058777">http://dbpedia.org/class/yago/EnlistedPerson110058777</a></td>
<td>25</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Soldier110622053">http://dbpedia.org/class/yago/Soldier110622053</a></td>
<td>25</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Referee110514429">http://dbpedia.org/class/yago/Referee110514429</a></td>
<td>25</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/BadPerson109831962">http://dbpedia.org/class/yago/BadPerson109831962</a></td>
<td>23</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Wrongdoer109633969">http://dbpedia.org/class/yago/Wrongdoer109633969</a></td>
<td>23</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Writer110794014">http://dbpedia.org/class/yago/Writer110794014</a></td>
<td>21</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Professional110480253">http://dbpedia.org/class/yago/Professional110480253</a></td>
<td>21</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/EnglishCricketers">http://dbpedia.org/class/yago/EnglishCricketers</a></td>
<td>20</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/OlympicBronzeMedalistsForGermany">http://dbpedia.org/class/yago/OlympicBronzeMedalistsForGermany</a></td>
<td>18</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Creator109614315">http://dbpedia.org/class/yago/Creator109614315</a></td>
<td>18</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/AsianGamesGoldMedalistsForIran">http://dbpedia.org/class/yago/AsianGamesGoldMedalistsForIran</a></td>
<td>16</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/OlympicGoldMedalistsForTheUnitedStates">http://dbpedia.org/class/yago/OlympicGoldMedalistsForTheUnitedStates</a></td>
<td>16</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Presenter110466387">http://dbpedia.org/class/yago/Presenter110466387</a></td>
<td>16</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Survivor110681194">http://dbpedia.org/class/yago/Survivor110681194</a></td>
<td>16</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Entertainer109616922">http://dbpedia.org/class/yago/Entertainer109616922</a></td>
<td>16</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Performer110415638">http://dbpedia.org/class/yago/Performer110415638</a></td>
<td>16</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Captain109893191">http://dbpedia.org/class/yago/Captain109893191</a></td>
<td>15</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/CommissionedMilitaryOfficer109943239">http://dbpedia.org/class/yago/CommissionedMilitaryOfficer109943239</a></td>
<td>15</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/CommissionedOfficer109942970">http://dbpedia.org/class/yago/CommissionedOfficer109942970</a></td>
<td>15</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Artist109812338">http://dbpedia.org/class/yago/Artist109812338</a></td>
<td>15</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/OlympicSilverMedalistsForParaguay">http://dbpedia.org/class/yago/OlympicSilverMedalistsForParaguay</a></td>
<td>14</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/OlympicSilverMedalistsForPoland">http://dbpedia.org/class/yago/OlympicSilverMedalistsForPoland</a></td>
<td>14</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Journalist110224578">http://dbpedia.org/class/yago/Journalist110224578</a></td>
<td>14</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Principal110474950">http://dbpedia.org/class/yago/Principal110474950</a></td>
<td>13</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/Criminal109977660">http://dbpedia.org/class/yago/Criminal109977660</a></td>
<td>13</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/OlympicGoldMedalistsForArgentina">http://dbpedia.org/class/yago/OlympicGoldMedalistsForArgentina</a></td>
<td>12</td>
</tr>
<tr>
<td><a href="http://dbpedia.org/class/yago/OlympicBronzeMedalistsForItaly">http://dbpedia.org/class/yago/OlympicBronzeMedalistsForItaly</a></td>
<td>12</td>
</tr>
</tbody></table>
Please find the entire list in the references [5].<br />
<br />
<b>Fact No. 4: There are more intellectuals among football players than bad persons.</b><br />
<br />
Wow, 237 football players are also categorized as being <b>intellectuals</b>, while 23 football players are listed as "bad persons". But, in this statistics, we will also find 21 writers(!) among the football players, as well as 15 artists, 14 journalists, and 13 criminals.<br />
Further down the list, you will also find<br />
<ul>
<li>12 politicians, </li>
<li>8 identical twins, </li>
<li>8(!) head of state, </li>
<li>7 musicians, </li>
<li>4 comedians (I'll bet there are more...), </li>
<li>4 scientists, </li>
<li>3 singers, </li>
<li>2 mammals,</li>
<li>2 Gentleman Cricketeers,</li>
<li>2 gambling addicts,</li>
<li>2 aviators,</li>
<li>2 painters,</li>
<li>1 UFO conspiracy theorists,</li>
<li>1 bank robber,</li>
<li>1 rapper,</li>
<li>1 plumber,</li>
</ul>
<br />
etc.<br />
<br />
So what does this tell us about football players?<br />
<br />
<b>Fact No. 5: There are more politicians among football players than comedians.</b><br />
<b><br /></b>
Well, in general, and according to Wikipedia, football players most times stick to their original profession. While emigrating here and there sometimes, there are only a few among them who actually have a second career outside of their original profession. Please note that we did not follow categories like football manager, football trainer, football coach, etc. Anyway, we finally did find also some authors among them....<br />
<b><br /></b>
<b>Fact No. 6: There are writers among the football players...although they are not listed as authors.</b><br />
<div>
<br /></div>
<div>
to be continued....</div>
<div>
<br /></div>
<div>
<br /></div>
<b>Please find the full tables with all the results listed here in the References:</b><br />
<br />
[1] total number of authors (<span style="font-family: Courier New, Courier, monospace;"><a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+COUNT%28DISTINCT+%3Fperson%29%0D%0AWHERE%0D%0A%7B%0D%0A+++%3Fperson+rdf%3Atype+dbpedia-owl%3AWriter+.%0D%0A%7D&format=text%2Fhtml&timeout=30000&debug=on">?author rdf:type dbpedia-owl:Writer</a> .</span>)<br />
[2] total number of football players (<span style="font-family: Courier New, Courier, monospace;"><a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+COUNT%28DISTINCT+%3Fperson%29%0D%0AWHERE%0D%0A%7B%0D%0A+++%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+.%0D%0A%7D&format=text%2Fhtml&timeout=30000&debug=on">?player rdf:type dbpedia-owl:SoccerPlayer</a> .</span>)<br />
[3] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+%3Ftype+COUNT%28DISTINCT+%3Fperson%29%0D%0AWHERE+%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+.%0D%0A+%3Fperson+rdf%3Atype+%3Ftype+FILTER+%28regex%28%3Ftype%2C%22expatriate%22%2C%22i%22%29%26%26%21regex%28%3Ftype%2C%22footballersIn%22%2C%22i%22%29%29%0D%0A%7D%0D%0AGROUP+BY+%3Ftype%0D%0AHAVING+COUNT%28DISTINCT+%3Fperson%29%3C20000%0D%0AORDER+by+DESC%28COUNT%28DISTINCT+%3Fperson%29%29&format=text%2Fhtml&timeout=30000&debug=on">expatriate football players by home country</a><br />
[4]<a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+%3Ftype+COUNT%28DISTINCT+%3Fperson%29%0D%0AWHERE+%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+.%0D%0A+%3Fperson+rdf%3Atype+%3Ftype+FILTER+%28regex%28%3Ftype%2C+%22footballersIn%7Csoccerplayersin%22%2C%22i%22%29%29%0D%0A%7D%0D%0AGROUP+BY+%3Ftype%0D%0AHAVING+COUNT%28DISTINCT+%3Fperson%29%3C20000%0D%0AORDER+by+DESC%28COUNT%28DISTINCT+%3Fperson%29%29&format=text%2Fhtml&timeout=30000&debug=on"> expatriate football players by emigration country</a><br />
[5] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+%3Ftype+COUNT%28DISTINCT+%3Fperson%29%0D%0AWHERE+%7B%0D%0A+%3Fperson+rdf%3Atype+dbpedia-owl%3ASoccerPlayer+.%0D%0A+%3Fperson+rdf%3Atype+%3Ftype+FILTER+%28%21regex%28%3Ftype%2C%22player%7Cfootball%7Cpeople%7Csoccer%7Cexpatriate%7Cemigrant%7Ccitizen%22%2C%22i%22%29%29%0D%0A%7D%0D%0AGROUP+BY+%3Ftype%0D%0AHAVING+COUNT%28DISTINCT+%3Fperson%29%3C20000%0D%0AORDER+by+DESC%28COUNT%28DISTINCT+%3Fperson%29%29&format=text%2Fhtml&timeout=30000&debug=on">occupations of football players other than football</a><br />
<br />Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-32202058343400978392014-06-22T17:12:00.002+02:002014-06-22T17:12:12.403+02:00Harald's Original Miscellany - Prolificacy vs. Popularity - Part 3<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: justify;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTRMgPLvGRCcb55XDHQFVwXK-Ca-hiGtPHMsjYctvR2ZkEr6Rj0ZXPdh_lrl23v8tvXFczHFI6DKogzMHTrFV8sVnJbdtO_nMp40PxeEI2XCnuIvD4ZHmc8mD0c1i24eiQYLPuiw/s1600/1200px-Old_book_-_Basking_Ridge_Historical_Society.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTRMgPLvGRCcb55XDHQFVwXK-Ca-hiGtPHMsjYctvR2ZkEr6Rj0ZXPdh_lrl23v8tvXFczHFI6DKogzMHTrFV8sVnJbdtO_nMp40PxeEI2XCnuIvD4ZHmc8mD0c1i24eiQYLPuiw/s1600/1200px-Old_book_-_Basking_Ridge_Historical_Society.jpg" height="212" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">photo: wikipedia</td></tr>
</tbody></table>
<div style="text-align: justify;">
So we were stranded with the problem that <i>The Lord of the Rings</i> was (of course) a notable work of <b>J.R.R. Tolkien</b>, but DBpedia said that <i>The Lord of the Rings</i> is not a "book", but it consists of 3 books [1,2]. The problem then is that filtering "notable works" with "books" cuts out book series. If we don't use the filter "notable works", then we will have also paintings, photographs, sculptures, etc. in our result list. </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
Let's find out about books in general in DBpedia. How many are there anyway? If we simply ask for all entities of the type "book", then we end up with currently <b>28,128 books </b>[3]. If we are asking for all things that have an author, then we end up with <b>63.071</b> [4] or <b>71.046</b> [5] depending on the way we ask. OK, not everything what is authored by somebody is also a book. There might be short stories, essays, articles, but also series of books. Ok, say we don't care and try it with all these kind of written works. Moreover, let's also consider the overall impact of all the written works of an author (by simply sum up all indegrees (=popularity) of her books). The next table shows the <b>Top 40 authors list based on all written works of the author and ordered by the overall impact </b>(GrandTotal) [6]:</div>
<div style="text-align: justify;">
<br /></div>
<table border="1" class="sparql" style="text-align: justify;">
<tbody>
<tr>
<th>name</th>
<th>numOfWorks</th>
<th>popularityOfWorks</th>
<th>GrandTotal</th>
<th>authDegree</th>
</tr>
<tr>
<td>"Charles Dickens"@en</td>
<td>8</td>
<td>398.7</td>
<td>3190</td>
<td>4026</td>
</tr>
<tr>
<td>"J. R. R. Tolkien"@en</td>
<td>3</td>
<td>819.2</td>
<td>2964</td>
<td>2859</td>
</tr>
<tr>
<td>"Elizabeth Sarnoff"@en</td>
<td>1</td>
<td>2457.0</td>
<td>2457</td>
<td>99</td>
</tr>
<tr>
<td>"Robin Green"@en</td>
<td>1</td>
<td>2277.0</td>
<td>2277</td>
<td>130</td>
</tr>
<tr>
<td>"Lewis Carroll"@en</td>
<td>2</td>
<td>939.0</td>
<td>1878</td>
<td>1576</td>
</tr>
<tr>
<td>"J. K. Rowling"@en</td>
<td>1</td>
<td>1829.0</td>
<td>1829</td>
<td>983</td>
</tr>
<tr>
<td>"Michael Stewart (playwright)"@en</td>
<td>6</td>
<td>233.6</td>
<td>1402</td>
<td>120</td>
</tr>
<tr>
<td>"Robert Louis Stevenson"@en</td>
<td>3</td>
<td>448.0</td>
<td>1344</td>
<td>1457</td>
</tr>
<tr>
<td>"George Orwell"@en</td>
<td>4</td>
<td>363.7</td>
<td>1309</td>
<td>1687</td>
</tr>
<tr>
<td>"Arthur Miller"@en</td>
<td>4</td>
<td>319.2</td>
<td>1277</td>
<td>1077</td>
</tr>
<tr>
<td>"Miguel de Cervantes"@en</td>
<td>2</td>
<td>611.0</td>
<td>1222</td>
<td>992</td>
</tr>
<tr>
<td>"Henrik Ibsen"@en</td>
<td>4</td>
<td>299.7</td>
<td>1199</td>
<td>1630</td>
</tr>
<tr>
<td>"Bram Stoker"@en</td>
<td>1</td>
<td>1145.0</td>
<td>1145</td>
<td>717</td>
</tr>
<tr>
<td>"Stephen King"@en</td>
<td>7</td>
<td>146.4</td>
<td>1105</td>
<td>2906</td>
</tr>
<tr>
<td>"Oscar Wilde"@en</td>
<td>2</td>
<td>486.3</td>
<td>1032</td>
<td>2324</td>
</tr>
<tr>
<td>"Samuel Beckett"@en</td>
<td>8</td>
<td>83.2</td>
<td>889</td>
<td>1414</td>
</tr>
<tr>
<td>"C. S. Lewis"@en</td>
<td>6</td>
<td>105.4</td>
<td>881</td>
<td>1530</td>
</tr>
<tr>
<td>"Naoko Takeuchi"@en</td>
<td>1</td>
<td>875.0</td>
<td>875</td>
<td>133</td>
</tr>
<tr>
<td>"Alexandre Dumas"@en</td>
<td>2</td>
<td>427.5</td>
<td>855</td>
<td>1125</td>
</tr>
<tr>
<td>"Roald Dahl"@en</td>
<td>16</td>
<td>53.8</td>
<td>834</td>
<td>855</td>
</tr>
<tr>
<td>"Tony Barwick"@en</td>
<td>4</td>
<td>202.2</td>
<td>809</td>
<td>114</td>
</tr>
<tr>
<td>"Terry Pratchett"@en</td>
<td>2</td>
<td>292.6</td>
<td>809</td>
<td>1032</td>
</tr>
<tr>
<td>"Jeremy Lloyd"@en</td>
<td>6</td>
<td>129.0</td>
<td>774</td>
<td>251</td>
</tr>
<tr>
<td>"Jimmy Perry"@en</td>
<td>2</td>
<td>382.5</td>
<td>765</td>
<td>84</td>
</tr>
<tr>
<td>"Victoria Morrow"@en</td>
<td>1</td>
<td>760.0</td>
<td>760</td>
<td>12</td>
</tr>
<tr>
<td>"Leo Tolstoy"@en</td>
<td>2</td>
<td>374.5</td>
<td>749</td>
<td>1927</td>
</tr>
<tr>
<td>"Dan Brown"@en</td>
<td>3</td>
<td>224.3</td>
<td>673</td>
<td>387</td>
</tr>
<tr>
<td>"John Steinbeck"@en</td>
<td>3</td>
<td>218.0</td>
<td>654</td>
<td>984</td>
</tr>
<tr>
<td>"Mark Twain"@en</td>
<td>2</td>
<td>333.6</td>
<td>615</td>
<td>2426</td>
</tr>
<tr>
<td>"Isaac Asimov"@en</td>
<td>4</td>
<td>123.7</td>
<td>607</td>
<td>2026</td>
</tr>
<tr>
<td>"Jonathan Swift"@en</td>
<td>2</td>
<td>303.5</td>
<td>607</td>
<td>1190</td>
</tr>
<tr>
<td>"Joseph Conrad"@en</td>
<td>11</td>
<td>59.4</td>
<td>603</td>
<td>909</td>
</tr>
<tr>
<td>"Tsugumi Ohba"@en</td>
<td>2</td>
<td>283.0</td>
<td>566</td>
<td>62</td>
</tr>
<tr>
<td>"Vladimir Nabokov"@en</td>
<td>4</td>
<td>157.1</td>
<td>535</td>
<td>1015</td>
</tr>
<tr>
<td>"Yoshihiro Togashi"@en</td>
<td>2</td>
<td>266.5</td>
<td>533</td>
<td>70</td>
</tr>
<tr>
<td>"Ryukishi07"@en</td>
<td>2</td>
<td>264.5</td>
<td>529</td>
<td>57</td>
</tr>
<tr>
<td>"Aldous Huxley"@en</td>
<td>3</td>
<td>175.3</td>
<td>526</td>
<td>968</td>
</tr>
<tr>
<td>"Dustin Lance Black"@en</td>
<td>2</td>
<td>262.5</td>
<td>525</td>
<td>159</td>
</tr>
<tr>
<td>"Rudyard Kipling"@en</td>
<td>3</td>
<td>196.2</td>
<td>520</td>
<td>1829</td>
</tr>
<tr>
<td>"Charlotte Brontë"@en</td>
<td>2</td>
<td>258.0</td>
<td>516</td>
<td>510</td>
</tr>
</tbody></table>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
The column "<i>authDegree</i>" denotes the popularity of the author alone (measured by the indegree of the author's article in Wikipedia). We notice that the situation has changed since our last experiment. <b>Tolkien</b> is now reported with 3 books (denoting that <i>The Lord of the Rings</i> is now included in his notable works list). Interestingly, he is now leading the list together with Charles Dickens, followed by <b>Elizabeth Sarnoff</b> and <b>Robin Green</b>, who only are mentioned with one notable work. Never heard of the later two? Well, Elizabeth Sarnoff is a writer for tv series as e.g. <i>Lost</i>, while Robin Green was writer and producer of <i>The Sopranos</i>. By opening the category "books" we now also have screen writers in our list, and the popularity of tv series seems to be rather significant, at least compared to literature.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
The same holds for <b>Naoko Takeuchi</b>. Ever heard about her? Well, maybe you don't. But, then you probably will know <i>Sailor Moon</i>, a very popular Japanese manga series. Yes, now we also include what belongs our contemporary literary culture: tv series and comics. Naoko Takeuchi between <b>C.S. Lewis</b> (<i>The Narnia Chronicles</i>) and <b>Alexandre Dumas</b> (<i>The Three Musketeers</i>). That fits well, doesn't it? If you look at the last column (authDegree), you will notice that this value is significant lower for Elizabeth Sarnoff, Robin Green, and Naoko Takeuchi as compared with C.S. Lewis or Alexandre Dumas. So maybe there works are currently rather popular, but in total the cultural influence of the already established writers of the past seems to have more impact.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
As a last question - and then I won't bother you with this statistics again - lets <b>reorder the current table according to the authors general impac</b>t (authDegree) [7].</div>
<div style="text-align: justify;">
<br /></div>
<table border="1" class="sparql" style="text-align: justify;">
<tbody>
<tr>
<th>name</th>
<th>numOfWorks</th>
<th>popularityOfWorks</th>
<th>GrandTotal</th>
<th>authDegree</th>
</tr>
<tr>
<td>"Charles Dickens"@en</td>
<td>8</td>
<td>398.7</td>
<td>3190</td>
<td>4026</td>
</tr>
<tr>
<td>"Stephen King"@en</td>
<td>7</td>
<td>146.4</td>
<td>1105</td>
<td>2906</td>
</tr>
<tr>
<td>"J. R. R. Tolkien"@en</td>
<td>3</td>
<td>819.2</td>
<td>2964</td>
<td>2859</td>
</tr>
<tr>
<td>"Johann Wolfgang von Goethe"@en</td>
<td>5</td>
<td>73.8</td>
<td>363</td>
<td>2734</td>
</tr>
<tr>
<td>"Cicero"@en</td>
<td>1</td>
<td>23.0</td>
<td>23</td>
<td>2469</td>
</tr>
<tr>
<td>"Mark Twain"@en</td>
<td>2</td>
<td>333.6</td>
<td>615</td>
<td>2426</td>
</tr>
<tr>
<td>"Oscar Wilde"@en</td>
<td>2</td>
<td>486.3</td>
<td>1032</td>
<td>2324</td>
</tr>
<tr>
<td>"Isaac Asimov"@en</td>
<td>4</td>
<td>123.7</td>
<td>607</td>
<td>2026</td>
</tr>
<tr>
<td>"H. P. Lovecraft"@en</td>
<td>3</td>
<td>101.3</td>
<td>304</td>
<td>2024</td>
</tr>
<tr>
<td>"T. S. Eliot"@en</td>
<td>3</td>
<td>169.8</td>
<td>477</td>
<td>1995</td>
</tr>
<tr>
<td>"Leo Tolstoy"@en</td>
<td>2</td>
<td>374.5</td>
<td>749</td>
<td>1927</td>
</tr>
<tr>
<td>"Arthur Conan Doyle"@en</td>
<td>1</td>
<td>130.0</td>
<td>130</td>
<td>1882</td>
</tr>
<tr>
<td>"Rudyard Kipling"@en</td>
<td>3</td>
<td>196.2</td>
<td>520</td>
<td>1829</td>
</tr>
<tr>
<td>"George Orwell"@en</td>
<td>4</td>
<td>363.7</td>
<td>1309</td>
<td>1687</td>
</tr>
<tr>
<td>"Henrik Ibsen"@en</td>
<td>4</td>
<td>299.7</td>
<td>1199</td>
<td>1630</td>
</tr>
<tr>
<td>"Neil Gaiman"@en</td>
<td>5</td>
<td>89.1</td>
<td>424</td>
<td>1589</td>
</tr>
<tr>
<td>"Lewis Carroll"@en</td>
<td>2</td>
<td>939.0</td>
<td>1878</td>
<td>1576</td>
</tr>
<tr>
<td>"C. S. Lewis"@en</td>
<td>6</td>
<td>105.4</td>
<td>881</td>
<td>1530</td>
</tr>
<tr>
<td>"Rabindranath Tagore"@en</td>
<td>4</td>
<td>67.8</td>
<td>276</td>
<td>1530</td>
</tr>
<tr>
<td>"Alan Moore"@en</td>
<td>1</td>
<td>17.0</td>
<td>17</td>
<td>1480</td>
</tr>
<tr>
<td>"Robert Louis Stevenson"@en</td>
<td>3</td>
<td>448.0</td>
<td>1344</td>
<td>1457</td>
</tr>
<tr>
<td>"Samuel Beckett"@en</td>
<td>8</td>
<td>83.2</td>
<td>889</td>
<td>1414</td>
</tr>
<tr>
<td>"Alexander Pushkin"@en</td>
<td>2</td>
<td>131.0</td>
<td>262</td>
<td>1372</td>
</tr>
<tr>
<td>"Philip K. Dick"@en</td>
<td>4</td>
<td>95.0</td>
<td>380</td>
<td>1259</td>
</tr>
<tr>
<td>"Arthur C. Clarke"@en</td>
<td>2</td>
<td>72.5</td>
<td>145</td>
<td>1252</td>
</tr>
<tr>
<td>"Ray Bradbury"@en</td>
<td>3</td>
<td>129.0</td>
<td>387</td>
<td>1249</td>
</tr>
<tr>
<td>"Robert E. Howard"@en</td>
<td>3</td>
<td>35.0</td>
<td>90</td>
<td>1223</td>
</tr>
<tr>
<td>"Jonathan Swift"@en</td>
<td>2</td>
<td>303.5</td>
<td>607</td>
<td>1190</td>
</tr>
<tr>
<td>"William Wordsworth"@en</td>
<td>1</td>
<td>79.0</td>
<td>79</td>
<td>1164</td>
</tr>
<tr>
<td>"Henry James"@en</td>
<td>6</td>
<td>74.0</td>
<td>444</td>
<td>1128</td>
</tr>
<tr>
<td>"Alexandre Dumas"@en</td>
<td>2</td>
<td>427.5</td>
<td>855</td>
<td>1125</td>
</tr>
<tr>
<td>"William S. Burroughs"@en</td>
<td>1</td>
<td>159.0</td>
<td>159</td>
<td>1119</td>
</tr>
<tr>
<td>"Arthur Miller"@en</td>
<td>4</td>
<td>319.2</td>
<td>1277</td>
<td>1077</td>
</tr>
<tr>
<td>"Jack Kerouac"@en</td>
<td>3</td>
<td>123.0</td>
<td>369</td>
<td>1034</td>
</tr>
<tr>
<td>"Terry Pratchett"@en</td>
<td>2</td>
<td>292.6</td>
<td>809</td>
<td>1032</td>
</tr>
<tr>
<td>"Virginia Woolf"@en</td>
<td>3</td>
<td>89.6</td>
<td>269</td>
<td>1026</td>
</tr>
<tr>
<td>"Vladimir Nabokov"@en</td>
<td>4</td>
<td>157.1</td>
<td>535</td>
<td>1015</td>
</tr>
<tr>
<td>"Miguel de Cervantes"@en</td>
<td>2</td>
<td>611.0</td>
<td>1222</td>
<td>992</td>
</tr>
<tr>
<td>"John Steinbeck"@en</td>
<td>3</td>
<td>218.0</td>
<td>654</td>
<td>984</td>
</tr>
<tr>
<td>"J. K. Rowling"@en</td>
<td>1</td>
<td>1829.0</td>
<td>1829</td>
<td>983</td>
</tr>
</tbody></table>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
Now we also see authors that haven't shown up in the first place because their single works were somehow too insignificant, but their overall impact wasn't. <b>Goethe</b>, <b>Mark Twain</b>, but also <b>Stephen King</b>, <b>Cicero</b>, or <b>Arthur Conan Doyle</b> are then among the top ranked authors. But no writers of tv shows or any mangas.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
Well, you might ask why it should make sense to make a statistics like that when you get so many different and in the end confusing results. Which one should we trust? If you ask me, trust none of them. All data is insufficient and doesn't reflect the "whole story", esp. in DBpedia (which is again only a fraction of all information available in Wikipedia, which again only reflects one (or some) specific viewpoints of reality. Thus the old saying is reinforced again: <b>Don't ever trust statistics that you haven't falsified yourself. </b></div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
[1] <a href="http://moresemantic.blogspot.de/2014/06/haralds-original-miscellany-prolificacy.html">Harald's Original Miscellany - Prolificacy vs. Popularity - Part 1</a></div>
<div style="text-align: justify;">
[2] <a href="http://moresemantic.blogspot.de/2014/06/haralds-original-miscellany-prolificacy_21.html">Harald's Original Miscellany - Prolificacy vs. Popularity - Part 2</a></div>
<div style="text-align: justify;">
[3] Number of all books in DBpedia, -><a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+COUNT%28DISTINCT+%3Fbook%29%0D%0AWHERE%0D%0A%7B%0D%0A++%3Fbook++rdf%3Atype+dbpedia-owl%3ABook+.%0D%0A%7D%0D%0A&format=text%2Fhtml&timeout=30000&debug=on"> <span style="font-family: Courier New, Courier, monospace;">?book rdf:type dbpedia:Book</span></a> .</div>
<div style="text-align: justify;">
[4] Number of books by counting what is authored -> <span style="font-family: Courier New, Courier, monospace;"><a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+COUNT%28DISTINCT+%3Fbook%29%0D%0AWHERE%0D%0A%7B%0D%0A++%3Fbook++dbpedia-owl%3Aauthor+%3Fauthor+.%0D%0A%7D%0D%0A&format=text%2Fhtml&timeout=30000&debug=on">?book dbpedia-owl:author ?author .</a></span></div>
<div style="text-align: justify;">
[5] Number of books by counting what is authored, using <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+COUNT%28DISTINCT+%3Fbook%29%0D%0AWHERE%0D%0A%7B%0D%0A%7B%0D%0A++%3Fbook++dbpedia-owl%3Aauthor+%3Fauthor+.%0D%0A%7D%0D%0AUNION%0D%0A%7B%0D%0A++%3Fbook+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2Fauthor%3E+%3Fauthor.%0D%0A%7D%0D%0A%7D%0D%0A&format=text%2Fhtml&timeout=30000&debug=on"><span style="font-family: Courier New, Courier, monospace;">dbpedia-owl:author</span> or <span style="font-family: Courier New, Courier, monospace;">dbprop:author</span></a></div>
<div style="text-align: justify;">
[6] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+%3Fname+COUNT%28DISTINCT+%3Fwork%29+as+%3FnumOfWorks+xsd%3Afloat%28xsd%3Ainteger%28AVG%28%3Findegree%29*10%29%29%2F10+as+%3FpopularityOfWorks+%28SUM%28DISTINCT+%3Findegree%29%29+as+%3FGrandTotal+AVG%28%3FauthorIndegree%29+as+%3FauthDegree%0D%0AWHERE+%7B%0D%0A%7B%0D%0A++%7B%0D%0A++++%3Fwork+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2Fauthor%3E+%3Fauthor.%0D%0A++%7D%0D%0A++UNION%0D%0A++%7B%0D%0A++++%3Fwork+dbpedia-owl%3Aauthor+%3Fauthor.%0D%0A++%7D%0D%0A%7D%0D%0A%23+++++++%3Fwork+rdf%3Atype+dbpedia-owl%3AWrittenWork+.%0D%0A+++++++%3Fwork+dbpedia-owl%3AwikiPageInLinkCount+%3Findegree+.%0D%0A+++++++%3Fauthor+dbpedia-owl%3AnotableWork+%3Fwork+.%0D%0A%23+++++++%3Fauthor+rdf%3Atype+dbpedia-owl%3AWriter+.%0D%0A+++++++%3Fauthor+dbpedia-owl%3AwikiPageInLinkCount+%3FauthorIndegree+.%0D%0A+++++++%3Fauthor+rdfs%3Alabel+%3Fname+FILTER+%28lang%28%3Fname%29%3D%22en%22%29+.%0D%0A%7D%0D%0AGROUP+BY+%3Fname%0D%0AORDER+BY+DESC%28%3FGrandTotal%29%0D%0ALIMIT+40&format=text%2Fhtml&timeout=30000&debug=on">Top 40 authors including all written works ordered by total impact of works</a></div>
<div style="text-align: justify;">
[7] <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+%3Fname+COUNT%28DISTINCT+%3Fwork%29+as+%3FnumOfWorks+xsd%3Afloat%28xsd%3Ainteger%28AVG%28%3Findegree%29*10%29%29%2F10+as+%3FpopularityOfWorks+%28SUM%28DISTINCT+%3Findegree%29%29+as+%3FGrandTotal+AVG%28%3FauthorIndegree%29+as+%3FauthDegree%0D%0AWHERE+%7B%0D%0A%7B%0D%0A++%7B%0D%0A++++%3Fwork+%3Chttp%3A%2F%2Fdbpedia.org%2Fproperty%2Fauthor%3E+%3Fauthor.%0D%0A++%7D%0D%0A++UNION%0D%0A++%7B%0D%0A++++%3Fwork+dbpedia-owl%3Aauthor+%3Fauthor.%0D%0A++%7D%0D%0A%7D%0D%0A%23+++++++%3Fwork+rdf%3Atype+dbpedia-owl%3AWrittenWork+.%0D%0A+++++++%3Fwork+dbpedia-owl%3AwikiPageInLinkCount+%3Findegree+.%0D%0A+++++++%3Fauthor+dbpedia-owl%3AnotableWork+%3Fwork+.%0D%0A%23+++++++%3Fauthor+rdf%3Atype+dbpedia-owl%3AWriter+.%0D%0A+++++++%3Fauthor+dbpedia-owl%3AwikiPageInLinkCount+%3FauthorIndegree+.%0D%0A+++++++%3Fauthor+rdfs%3Alabel+%3Fname+FILTER+%28lang%28%3Fname%29%3D%22en%22%29+.%0D%0A%7D%0D%0AGROUP+BY+%3Fname%0D%0AORDER+BY+DESC%28%3FauthDegree%29%0D%0ALIMIT+40&format=text%2Fhtml&timeout=30000&debug=on">Top 40 authors ordered by total impact of the authors including notable works and their impact</a></div>
<div style="text-align: justify;">
<br /></div>
Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-2618628308170331302014-06-21T11:18:00.003+02:002014-06-21T11:26:51.639+02:00Harald's Original Miscellany - Prolificacy vs. Popularity in Literature, Part 2<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPlBmdPx-HtJjWDbAW0Gbh9SwMQooMaHmXwr8WAVK_91v3yG4ydpi5RGSaYSYhSxcKX66D9bw2bQDKHGxc0hyphenhyphenGkgqykO7CoY39NHjDYSf1KGZztzeJ20aDftigMmgItcSe8xo-aQ/s1600/nicebooks.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPlBmdPx-HtJjWDbAW0Gbh9SwMQooMaHmXwr8WAVK_91v3yG4ydpi5RGSaYSYhSxcKX66D9bw2bQDKHGxc0hyphenhyphenGkgqykO7CoY39NHjDYSf1KGZztzeJ20aDftigMmgItcSe8xo-aQ/s1600/nicebooks.jpg" height="237" width="320" /></a></div>
It might have been a surprise for you that according to <a href="http://moresemantic.blogspot.de/2014/06/haralds-original-miscellany-prolificacy.html">Wikipedia, editor in chief of the Oxford English Dictionary John Simpson is the most popular author</a> [1]. Thus, we have to take a look on the notion of "<i>popularity</i>". In scientific publishing, an "<i>important</i>" author is an author whose works are cited (referenced) by many other authors. In this way, a ranking among the most important authors has been established. In Wikipedia, we can follow this approach and simply count, how many articles are referencing the article of an author or the articles of the author`s books. In our approach, this was simply the number of incoming pageLinks of the referenced articles. Currently, we are adopting the <a href="http://en.wikipedia.org/wiki/PageRank">PageRank</a> algorithm for achieving a better measure of "importance" of an article. You all know the PageRank algorithm named after Larry Page, one of the founders of Google. PageRank is a way of measuring the importance of website pages [2]. We will get back on this, when we have finished PageRank integration into DBpedia.<br />
<br />
But, let's discuss the results we had achieved in our last blog post. We stated that there are <b>15,328 authors</b> [3] referenced in DBpedia. Well, of course there might be more authors, but this is the number of individuals that belong to the DBpedia class authors. The question is, when we made the popularity vs prolificacy statistics, did we include the works of 15,328 authors? Surely not. Let's first check, for how many authors, there are books written by these authors referenced in DBpedia: Its only <b>3,763 authors </b>with referenced books [4]. Thus, we don't have any idea about the remaining 11,000 authors who don't have one of their books referenced in DBpedia. They must have written some book, otherwise they would not be an "author"....<br />
<br />
OK, so we are dealing with insufficient information. If we look at the available data, we could also use another property, called <span style="font-family: Courier New, Courier, monospace;">dbpedia-owl:notableWork</span> denoting only the "<i>important</i>" works of an author (if we relate it with authors and not with other artists). Now it would be interesting, to repeat our statistics based on this property and look, if there is a significant difference. First let's look at the <b>most prolific authors with respect to referenced "notable works":</b><br />
<br />
<table border="1" class="sparql">
<tbody>
<tr>
<th>name</th>
<th>numOfNotableWorks</th>
<th>popularityOfNotableWorks</th>
</tr>
<tr>
<td>"Roald Dahl"@en</td>
<td>16</td>
<td>53.8</td>
</tr>
<tr>
<td>"Joseph Conrad"@en</td>
<td>9</td>
<td>68.2</td>
</tr>
<tr>
<td>"Charles Dickens"@en</td>
<td>8</td>
<td>398.7</td>
</tr>
<tr>
<td>"Roger Zelazny"@en</td>
<td>7</td>
<td>15.7</td>
</tr>
<tr>
<td>"Henryk Sienkiewicz"@en</td>
<td>6</td>
<td>53.8</td>
</tr>
<tr>
<td>"Tom Wolfe"@en</td>
<td>6</td>
<td>44.3</td>
</tr>
<tr>
<td>"Margaret Atwood"@en</td>
<td>6</td>
<td>41.8</td>
</tr>
<tr>
<td>"Henry James"@en</td>
<td>6</td>
<td>74.0</td>
</tr>
<tr>
<td>"Ismail Kadare"@en</td>
<td>6</td>
<td>7.8</td>
</tr>
<tr>
<td>"Alfred Döblin"@en</td>
<td>6</td>
<td>13.3</td>
</tr>
<tr>
<td>"Stephen King"@en</td>
<td>6</td>
<td>133.1</td>
</tr>
<tr>
<td>"Robert Girardi"@en</td>
<td>6</td>
<td>3.0</td>
</tr>
<tr>
<td>"Hunter S. Thompson"@en</td>
<td>5</td>
<td>56.4</td>
</tr>
<tr>
<td>"Joanne Harris"@en</td>
<td>5</td>
<td>16.4</td>
</tr>
<tr>
<td>"David Mitchell (author)"@en</td>
<td>5</td>
<td>25.6</td>
</tr>
<tr>
<td>"Chris Kuzneski"@en</td>
<td>5</td>
<td>7.0</td>
</tr>
<tr>
<td>"Jackie French"@en</td>
<td>5</td>
<td>8.2</td>
</tr>
<tr>
<td>"Sita Ram Goel"@en</td>
<td>5</td>
<td>7.0</td>
</tr>
<tr>
<td>"Peter Hitchens"@en</td>
<td>5</td>
<td>9.2</td>
</tr>
<tr>
<td>"Chinua Achebe"@en</td>
<td>5</td>
<td>31.6</td>
</tr>
<tr>
<td>"Colm Tóibín"@en</td>
<td>5</td>
<td>17.4</td>
</tr>
<tr>
<td>"E. L. Doctorow"@en</td>
<td>5</td>
<td>26.4</td>
</tr>
<tr>
<td>"Don DeLillo"@en</td>
<td>5</td>
<td>27.4</td>
</tr>
<tr>
<td>"Johann Wolfgang von Goethe"@en</td>
<td>5</td>
<td>72.6</td>
</tr>
<tr>
<td>"Samuel Beckett"@en</td>
<td>5</td>
<td>25.4</td>
</tr>
<tr>
<td>"Cormac McCarthy"@en</td>
<td>5</td>
<td>55.6</td>
</tr>
<tr>
<td>"Brian O'Nolan"@en</td>
<td>5</td>
<td>29.8</td>
</tr>
<tr>
<td>"Poppy Z. Brite"@en</td>
<td>5</td>
<td>11.2</td>
</tr>
<tr>
<td>"William Trevor"@en</td>
<td>5</td>
<td>20.2</td>
</tr>
<tr>
<td>"Neil Gaiman"@en</td>
<td>5</td>
<td>84.8</td>
</tr>
<tr>
<td>"Dean Koontz"@en</td>
<td>5</td>
<td>10.4</td>
</tr>
<tr>
<td>"George MacDonald"@en</td>
<td>5</td>
<td>21.6</td>
</tr>
<tr>
<td>"Ann Bannon"@en</td>
<td>5</td>
<td>9.0</td>
</tr>
<tr>
<td>"William Faulkner"@en</td>
<td>4</td>
<td>70.7</td>
</tr>
<tr>
<td>"Will Self"@en</td>
<td>4</td>
<td>10.0</td>
</tr>
<tr>
<td>"Alan Hollinghurst"@en</td>
<td>4</td>
<td>23.5</td>
</tr>
<tr>
<td>"George Eliot"@en</td>
<td>4</td>
<td>81.0</td>
</tr>
<tr>
<td>"Malcolm Gladwell"@en</td>
<td>4</td>
<td>39.0</td>
</tr>
<tr>
<td>"C. S. Lewis"@en</td>
<td>4</td>
<td>43.2</td>
</tr>
<tr>
<td>"Hermann Hesse"@en</td>
<td>4</td>
<td>53.2</td>
</tr>
</tbody></table>
<br />
There is a huge difference. Less science fiction authors, more international authors, and the average "popularity" of the top 40 author's works has also significantly increased. We also realize that considering <b>Charles Dickens,</b> the popularity of his 8 notable works (398) doesn't differ so much from the popularity of his overall 30 works (159.5) as for <b>Stephen King</b>, where the popularity of his 5 notable works (133) is much higher compared to the overall average from last time (44) for his 75 books. Interesting also that <b>Road Dahl</b> is mentioned with 16 notable works. Either the wikipedia authors could not decide which of the works of Road Dahl really was notable or the article must have been written by a huge fan. OK, so we might learn that is in the eye of the beholder, which works of an author are referenced as being "<i>notable</i>".<br />
<br />
Let's order the list now again according to the popularity measure and see what happens. This is the <b>Top 40 list of authors with the most popular works (on average) with respect to "notable works" only</b>:<br />
<br />
<table border="1" class="sparql">
<tbody>
<tr>
<th>name</th>
<th>numOfNotableWorks</th>
<th>popularityOfNotableWorks</th>
</tr>
<tr>
<td>"Bram Stoker"@en</td>
<td>1</td>
<td>1145.0</td>
</tr>
<tr>
<td>"Lewis Carroll"@en</td>
<td>2</td>
<td>939.0</td>
</tr>
<tr>
<td>"Lucifer Chu"@en</td>
<td>1</td>
<td>612.0</td>
</tr>
<tr>
<td>"Miguel de Cervantes"@en</td>
<td>2</td>
<td>611.0</td>
</tr>
<tr>
<td>"J. R. R. Tolkien"@en</td>
<td>2</td>
<td>566.0</td>
</tr>
<tr>
<td>"Robert Louis Stevenson"@en</td>
<td>3</td>
<td>448.0</td>
</tr>
<tr>
<td>"Alexandre Dumas"@en</td>
<td>2</td>
<td>427.5</td>
</tr>
<tr>
<td>"Oscar Wilde"@en</td>
<td>1</td>
<td>427.0</td>
</tr>
<tr>
<td>"Kenneth Grahame"@en</td>
<td>1</td>
<td>423.0</td>
</tr>
<tr>
<td>"Walter Prescott Webb"@en</td>
<td>1</td>
<td>423.0</td>
</tr>
<tr>
<td>"James Vincent Murphy"@en</td>
<td>1</td>
<td>421.0</td>
</tr>
<tr>
<td>"George Orwell"@en</td>
<td>3</td>
<td>412.3</td>
</tr>
<tr>
<td>"Charles Dickens"@en</td>
<td>8</td>
<td>398.7</td>
</tr>
<tr>
<td>"Emily Brontë"@en</td>
<td>1</td>
<td>387.0</td>
</tr>
<tr>
<td>"Leo Tolstoy"@en</td>
<td>2</td>
<td>374.5</td>
</tr>
<tr>
<td>"John Bunyan"@en</td>
<td>1</td>
<td>341.0</td>
</tr>
<tr>
<td>"Louisa May Alcott"@en</td>
<td>1</td>
<td>314.0</td>
</tr>
<tr>
<td>"Mark Twain"@en</td>
<td>2</td>
<td>307.5</td>
</tr>
<tr>
<td>"Jonathan Swift"@en</td>
<td>2</td>
<td>303.5</td>
</tr>
<tr>
<td>"Emma Orczy"@en</td>
<td>1</td>
<td>282.0</td>
</tr>
<tr>
<td>"Charlotte Brontë"@en</td>
<td>2</td>
<td>258.0</td>
</tr>
<tr>
<td>"Ian McFarlane"@en</td>
<td>1</td>
<td>255.0</td>
</tr>
<tr>
<td>"William Golding"@en</td>
<td>1</td>
<td>251.0</td>
</tr>
<tr>
<td>"Anne Frank"@en</td>
<td>1</td>
<td>246.0</td>
</tr>
<tr>
<td>"James Fenimore Cooper"@en</td>
<td>1</td>
<td>246.0</td>
</tr>
<tr>
<td>"Margaret Mitchell"@en</td>
<td>2</td>
<td>242.0</td>
</tr>
<tr>
<td>"Frans Sammut"@en</td>
<td>2</td>
<td>233.5</td>
</tr>
<tr>
<td>"Rudyard Kipling"@en</td>
<td>2</td>
<td>230.5</td>
</tr>
<tr>
<td>"Dan Brown"@en</td>
<td>3</td>
<td>224.3</td>
</tr>
<tr>
<td>"John Steinbeck"@en</td>
<td>3</td>
<td>218.0</td>
</tr>
<tr>
<td>"William Gibson"@en</td>
<td>1</td>
<td>216.0</td>
</tr>
<tr>
<td>"Ayn Rand"@en</td>
<td>2</td>
<td>210.5</td>
</tr>
<tr>
<td>"Harriet McDougal"@en</td>
<td>1</td>
<td>207.0</td>
</tr>
<tr>
<td>"Jim Butcher"@en</td>
<td>1</td>
<td>200.0</td>
</tr>
<tr>
<td>"Richard Adams"@en</td>
<td>1</td>
<td>192.0</td>
</tr>
<tr>
<td>"William Makepeace Thackeray"@en</td>
<td>1</td>
<td>191.0</td>
</tr>
<tr>
<td>"T. S. Eliot"@en</td>
<td>2</td>
<td>186.0</td>
</tr>
<tr>
<td>"Aldous Huxley"@en</td>
<td>3</td>
<td>175.3</td>
</tr>
<tr>
<td>"Hitoshi Igarashi"@en</td>
<td>1</td>
<td>175.0</td>
</tr>
<tr>
<td>"Alice Walker"@en</td>
<td>1</td>
<td>174.0</td>
</tr>
</tbody></table>
<br />
This looks a lot more like we would have expect it to be in the first place. <b>Bram Stoker</b> on the first place with only 1 notable work, we all know which book is meant by that :) impressive score.<b> Lewis Carroll</b> on the second place with his Alice in Wonderland stories is also no wonder. But, who is <b>Lucifer Chu</b>? This is some kind of surprise for me. You won't find Lucifer Chu in the German version of DBpedia, so why does our result suggest that he is a popular author? Well, if you look at the very short Wikipedia article in the English version, you will find out that he is the author of the Chinese translation of <b>J.R.R.Tolkien's</b> <i>The Hobbit</i> and <i>The Lord of the Rings</i>. Tolkien's books follow on rank number 5 with a slightly less popularity. How can that be? Well we must examine the available data a little bit closer.<br />
<br />
Chu is referenced in our list with 1 book only although we know that he has translated <i>The Hobbit </i>as well as <i>The Lord of the Rings</i>. And J.R.R. Tolkien is referenced with 2 books. Obviously, for Chu only the Hobbit as being the most popular of these books has been counted. But, if we look at the DBpedia page of Lucifer Chu, we realize that <a href="http://dbpedia.org/page/Lucifer_Chu">for Chu there are 2 books referenced as notable Work</a>, as there are <a href="http://dbpedia.org/page/J._R._R._Tolkien">3 books for Tolkien </a>(including <i>The Silmarillion</i>). The reason for this is that <i>The Lord of the Rings</i> is not a member of the class <span style="font-family: Courier New, Courier, monospace;">dbpedia-owl:Book</span>. Why is that so? Maybe because <i>The Lord of the Rings</i> consists out of 3 different books. Thus, to get a more complete result, we should think of how we get all notable works into that list. But, we should simultaneously take care of only including books or series of books and to exclude other pieces or work such as e.g. paintings, sculptures, movies, photographs, etc.<br />
<br />
We will take care of this next time... :)<br />
<br />
<b>References:</b><br />
[1] <a href="http://moresemantic.blogspot.de/2014/06/haralds-original-miscellany-prolificacy.html">Harald's Original Miscellany - Prolificacy vs. Popularity in Literature, Part 1</a>, 2014/06/20<br />
[2] Brin, S.; Page, L. (1998). "<a href="http://infolab.stanford.edu/pub/papers/google.pdf">The anatomy of a large-scale hypertextual Web search engine</a>". Computer Networks and ISDN Systems 30: 107–117.<br />
[3] live SPARQL query for the <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+COUNT%28DISTINCT+%3Fs%29%0D%0AWHERE%0D%0A%7B%0D%0A++%3Fs+rdf%3Atype+dbpedia-owl%3AWriter+.%0D%0A%7D&format=text%2Fhtml&timeout=30000&debug=on">number or authors</a><br />
[4] live SPARQL query for the <a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+COUNT%28DISTINCT+%3Fs%29%0D%0AWHERE%0D%0A%7B%0D%0A++%3Fs+rdf%3Atype+dbpedia-owl%3AWriter+.%0D%0A++%3Fworks+dbpedia-owl%3Aauthor+%3Fs.%0D%0A%7D&format=text%2Fhtml&timeout=30000&debug=on">number of distinct authors who have written a book, referenced in DBpedia</a><br />
[5] live SPARQL query for the <b><a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+%3Fname+COUNT%28%3Fwork%29+as+%3FnumOfWorks+xsd%3Afloat%28xsd%3Ainteger%28AVG%28%3Findegree%29*10%29%29%2F10+as+%3FpopularityOfWorks%0D%0AWHERE+%7B%0D%0A+++++++%3Fwork+++rdf%3Atype+dbpedia-owl%3ABook+%3B%0D%0A+++++++++++++++dbpedia-owl%3AwikiPageInLinkCount+%3Findegree+.%0D%0A+++++++%3Fauthor+dbpedia-owl%3AnotableWork+%3Fwork+.%0D%0A+++++++%3Fauthor+rdfs%3Alabel+%3Fname+FILTER+%28lang%28%3Fname%29%3D%22en%22%29+.%0D%0A%7D%0D%0AGROUP+BY+%3Fname%0D%0AORDER+BY+DESC%28COUNT%28*%29%29&format=text%2Fhtml&timeout=30000&debug=on">Top 40 most prolific authors</a></b> (based on notable works)<br />
[6] live SPARQL query for the <b><a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+%3Fname+COUNT%28%3Fwork%29+as+%3FnumOfWorks+xsd%3Afloat%28xsd%3Ainteger%28AVG%28%3Findegree%29*10%29%29%2F10+as+%3FpopularityOfWorks%0D%0AWHERE+%7B%0D%0A+++++++%3Fwork+++rdf%3Atype+dbpedia-owl%3ABook+%3B%0D%0A+++++++++++++++dbpedia-owl%3AwikiPageInLinkCount+%3Findegree+.%0D%0A+++++++%3Fauthor+dbpedia-owl%3AnotableWork+%3Fwork+.%0D%0A+++++++%3Fauthor+rdfs%3Alabel+%3Fname+FILTER+%28lang%28%3Fname%29%3D%22en%22%29+.%0D%0A%7D%0D%0AGROUP+BY+%3Fname%0D%0AORDER+BY+DESC%28AVG%28%3Findegree%29%29&format=text%2Fhtml&timeout=30000&debug=on">Top 40 most popular authors </a></b>(based on notable works)<br />
<b><br /></b>Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-68772833779094144192014-06-20T13:30:00.000+02:002014-06-20T17:39:01.775+02:00Harald's Original Miscellany - Prolificacy vs. Popularity in Literature<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEihwCm12hMEDYr7f4wi5TBUIx1qvbu_zyb19h8mgYXSICk-8P1q1RgQxiNPCCNptyubxpn0ukV8BliMEMzs50XfHvTcXMQCnGViOtMMWtqf2wDA_JJ4Mhqic0akVyz_ghIlP3vR5g/s1600/books1.jpeg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEihwCm12hMEDYr7f4wi5TBUIx1qvbu_zyb19h8mgYXSICk-8P1q1RgQxiNPCCNptyubxpn0ukV8BliMEMzs50XfHvTcXMQCnGViOtMMWtqf2wDA_JJ4Mhqic0akVyz_ghIlP3vR5g/s1600/books1.jpeg" height="195" width="320" /></a></div>
Oh wow, it's quite a while that I wrote my last post here in the blog... But, while preparing exercises for the OpenHPI MOOC course '<a href="https://openhpi.de/courses/2d1ede48-4cc6-4a36-bcc4-6cb02e36b3ea">Knowledge Engineering with Semantic Technologies</a>', I was about to play around a little with SPARQL to come up with new exercises for the students of the course. To make it short, our current lecture examples all deal with writers and books. Thus, to learn how to query RDF knowledge bases with the SPARQL query language, I chose the DBpedia. While trying to think of some interesting toy examples, I started to play around and the facts that I discovered by chance were so interesting that I totally forgot about my lunch break :)<br />
<br />
So here are some interesting facts about books and authors that will be continued in later posts. All presented statistics is based on the (English) Wikipedia (of course for the SPARQL queries we use DBpedia)...but nevertheless, it is wikipedia knowledge.<br />
<br />
There are currently <b>15,328 authors</b> listed (<i>i.e. they are member of the class </i><span style="font-family: Courier New, Courier, monospace;">dbpedia-owl:Writer</span>). First thing I wanted to find out was, who are the most prolific authors according to Wikipedia (<i>at least this means, whose works also exist as Wikipedia Pages and who are connected via </i><span style="font-family: Courier New, Courier, monospace;">dbpedia-owl:author</span>).<br />
<br />
Well, here are the <b>Top 40 Most Prolific Writers</b>:<br />
<br />
<table border="1" class="sparql">
<tbody>
<tr>
<th>name</th>
<th>numOfWorks</th>
<th>popularityOfWorks</th>
</tr>
<tr>
<td>"L. Sprague de Camp"@en</td>
<td>128</td>
<td>10.9</td>
</tr>
<tr>
<td>"Agatha Christie"@en</td>
<td>103</td>
<td>32.7</td>
</tr>
<tr>
<td>"Isaac Asimov"@en</td>
<td>75</td>
<td>28.3</td>
</tr>
<tr>
<td>"Stephen King"@en</td>
<td>75</td>
<td>44.0</td>
</tr>
<tr>
<td>"Philip K. Dick"@en</td>
<td>74</td>
<td>14.4</td>
</tr>
<tr>
<td>"Edgar Rice Burroughs"@en</td>
<td>73</td>
<td>18.7</td>
</tr>
<tr>
<td>"Ruth Rendell"@en</td>
<td>70</td>
<td>5.3</td>
</tr>
<tr>
<td>"Dean Koontz"@en</td>
<td>67</td>
<td>6.2</td>
</tr>
<tr>
<td>"Lin Carter"@en</td>
<td>64</td>
<td>9.0</td>
</tr>
<tr>
<td>"Terry Pratchett"@en</td>
<td>63</td>
<td>41.7</td>
</tr>
<tr>
<td>"Jules Verne"@en</td>
<td>63</td>
<td>31.2</td>
</tr>
<tr>
<td>"P. G. Wodehouse"@en</td>
<td>63</td>
<td>20.9</td>
</tr>
<tr>
<td>"Robert E. Howard"@en</td>
<td>62</td>
<td>10.5</td>
</tr>
<tr>
<td>"Gary Paulsen"@en</td>
<td>61</td>
<td>4.1</td>
</tr>
<tr>
<td>"K. A. Applegate"@en</td>
<td>61</td>
<td>18.5</td>
</tr>
<tr>
<td>"August Derleth"@en</td>
<td>60</td>
<td>5.2</td>
</tr>
<tr>
<td>"John Dickson Carr"@en</td>
<td>59</td>
<td>5.9</td>
</tr>
<tr>
<td>"H. G. Wells"@en</td>
<td>57</td>
<td>30.0</td>
</tr>
<tr>
<td>"James Patterson"@en</td>
<td>56</td>
<td>12.0</td>
</tr>
<tr>
<td>"Leslie Charteris"@en</td>
<td>55</td>
<td>8.8</td>
</tr>
<tr>
<td>"Robert A. Heinlein"@en</td>
<td>52</td>
<td>36.5</td>
</tr>
<tr>
<td>"Rex Stout"@en</td>
<td>52</td>
<td>21.4</td>
</tr>
<tr>
<td>"Arthur C. Clarke"@en</td>
<td>50</td>
<td>20.8</td>
</tr>
<tr>
<td>"Harry Turtledove"@en</td>
<td>49</td>
<td>11.3</td>
</tr>
<tr>
<td>"Ray Bradbury"@en</td>
<td>49</td>
<td>15.3</td>
</tr>
<tr>
<td>"David Weber"@en</td>
<td>49</td>
<td>30.9</td>
</tr>
<tr>
<td>"Danielle Steel"@en</td>
<td>48</td>
<td>3.3</td>
</tr>
<tr>
<td>"J. M. G. Le Clézio"@en</td>
<td>48</td>
<td>3.7</td>
</tr>
<tr>
<td>"Henry James"@en</td>
<td>48</td>
<td>18.5</td>
</tr>
<tr>
<td>"Piers Anthony"@en</td>
<td>48</td>
<td>14.0</td>
</tr>
<tr>
<td>"Clive Cussler"@en</td>
<td>47</td>
<td>10.1</td>
</tr>
<tr>
<td>"Roger Zelazny"@en</td>
<td>45</td>
<td>10.5</td>
</tr>
<tr>
<td>"Alan Dean Foster"@en</td>
<td>44</td>
<td>6.7</td>
</tr>
<tr>
<td>"Joe R. Lansdale"@en</td>
<td>43</td>
<td>4.7</td>
</tr>
<tr>
<td>"Gordon R. Dickson"@en</td>
<td>41</td>
<td>5.0</td>
</tr>
<tr>
<td>"Marion Zimmer Bradley"@en</td>
<td>41</td>
<td>7.6</td>
</tr>
<tr>
<td>"Samuel R. Delany"@en</td>
<td>41</td>
<td>9.0</td>
</tr>
<tr>
<td>"Bernard Cornwell"@en</td>
<td>40</td>
<td>16.6</td>
</tr>
<tr>
<td>"Enid Blyton"@en</td>
<td>40</td>
<td>6.8</td>
</tr>
<tr>
<td>"Dr. Seuss"@en</td>
<td>40</td>
<td>26.5</td>
</tr>
</tbody></table>
The average Popularity Score that you see in the third column corresponds to the number of references (links) from other wikipedia articles to these books. Interestingly, <b>Agatha Christie</b> as well as <b>Isaac Asimov</b> are rather prolific authors whose books also have an above the average popularity. On the other hand, Ruth Rendell or Dean Koontz are rather prolific, but not very popular (at least according to wikipedia). Most popular in this list are <b>Stephen King</b> and <b>Terry Prachett</b>.<br />
<div>
<br /></div>
<div>
Well, let's turn it the other way around. Let's sort this list by the <b>average popularity of the books</b> of these authors....</div>
<div>
<br /></div>
<div>
Here is the <b>Top 40 list of the authors with the most popular books</b> (on average):</div>
<table border="1" class="sparql">
<tbody>
<tr>
<th>name</th>
<th>numOfWorks</th>
<th>popularityOfWorks</th>
</tr>
<tr>
<td>"John Simpson (lexicographer)"@en</td>
<td>1</td>
<td>1627.0</td>
</tr>
<tr>
<td>"John Milton"@en</td>
<td>1</td>
<td>669.0</td>
</tr>
<tr>
<td>"Kenneth Grahame"@en</td>
<td>1</td>
<td>423.0</td>
</tr>
<tr>
<td>"Emily Brontë"@en</td>
<td>1</td>
<td>387.0</td>
</tr>
<tr>
<td>"Wilhelm Grimm"@en</td>
<td>1</td>
<td>343.0</td>
</tr>
<tr>
<td>"Jacob Grimm"@en</td>
<td>1</td>
<td>343.0</td>
</tr>
<tr>
<td>"Harper Lee"@en</td>
<td>1</td>
<td>334.0</td>
</tr>
<tr>
<td>"Lewis Carroll"@en</td>
<td>6</td>
<td>319.8</td>
</tr>
<tr>
<td>"Miguel de Cervantes"@en</td>
<td>4</td>
<td>310.5</td>
</tr>
<tr>
<td>"Jonathan Swift"@en</td>
<td>2</td>
<td>303.5</td>
</tr>
<tr>
<td>"Wilbert Awdry"@en</td>
<td>1</td>
<td>296.0</td>
</tr>
<tr>
<td>"Cao Xueqin"@en</td>
<td>1</td>
<td>291.0</td>
</tr>
<tr>
<td>"Giovanni Boccaccio"@en</td>
<td>1</td>
<td>287.0</td>
</tr>
<tr>
<td>"William Shakespeare"@en</td>
<td>3</td>
<td>264.6</td>
</tr>
<tr>
<td>"Ian McFarlane"@en</td>
<td>1</td>
<td>255.0</td>
</tr>
<tr>
<td>"Antoine de Saint-Exupéry"@en</td>
<td>1</td>
<td>253.0</td>
</tr>
<tr>
<td>"Margaret Mitchell"@en</td>
<td>2</td>
<td>242.0</td>
</tr>
<tr>
<td>"Suetonius"@en</td>
<td>1</td>
<td>229.0</td>
</tr>
<tr>
<td>"Roger Hargreaves"@en</td>
<td>2</td>
<td>214.0</td>
</tr>
<tr>
<td>"Joseph O'Neill (writer)"@en</td>
<td>1</td>
<td>211.0</td>
</tr>
<tr>
<td>"T. S. Eliot"@en</td>
<td>2</td>
<td>186.0</td>
</tr>
<tr>
<td>"George Bernard Shaw"@en</td>
<td>2</td>
<td>184.5</td>
</tr>
<tr>
<td>"Harriet Beecher Stowe"@en</td>
<td>3</td>
<td>173.6</td>
</tr>
<tr>
<td>"Petronius"@en</td>
<td>1</td>
<td>162.0</td>
</tr>
<tr>
<td>"Charles Dickens"@en</td>
<td>30</td>
<td>159.5</td>
</tr>
<tr>
<td>"Johanna Spyri"@en</td>
<td>1</td>
<td>156.0</td>
</tr>
<tr>
<td>"Herman Melville"@en</td>
<td>7</td>
<td>154.7</td>
</tr>
<tr>
<td>"Jaroslav Hašek"@en</td>
<td>1</td>
<td>152.0</td>
</tr>
<tr>
<td>"Pierre Choderlos de Laclos"@en</td>
<td>1</td>
<td>152.0</td>
</tr>
<tr>
<td>"Oscar Wilde"@en</td>
<td>3</td>
<td>148.0</td>
</tr>
<tr>
<td>"Dave Arneson"@en</td>
<td>3</td>
<td>146.0</td>
</tr>
<tr>
<td>"Ngô Sĩ Liên"@en</td>
<td>1</td>
<td>145.0</td>
</tr>
<tr>
<td>"John Eric Holmes"@en</td>
<td>2</td>
<td>143.5</td>
</tr>
<tr>
<td>"Carlo Collodi"@en</td>
<td>1</td>
<td>142.0</td>
</tr>
<tr>
<td>"George Orwell"@en</td>
<td>11</td>
<td>135.0</td>
</tr>
<tr>
<td>"Erik Mona"@en</td>
<td>3</td>
<td>134.6</td>
</tr>
<tr>
<td>"Daniel Defoe"@en</td>
<td>5</td>
<td>132.8</td>
</tr>
<tr>
<td>"Monte Cook"@en</td>
<td>3</td>
<td>132.3</td>
</tr>
<tr>
<td>"Apuleius"@en</td>
<td>1</td>
<td>132.0</td>
</tr>
<tr>
<td>"Dan Brown"@en</td>
<td>6</td>
<td>126.6</td>
</tr>
</tbody></table>
Possibly you have never heard of John Simpson? But you will have heard about the <i>Oxford English Dictionary</i>. Well <b>John Simpson</b> was its Chief Editor...that makes sense, doesn't it? What about <b>Kenneth Graham</b>? Maybe you know his 1908 published novel <i>The Wind and the Willows</i>...<br />
<br />
In this list it seems that it is more about literary excellency. Only one author with a rather prolific output is found, which is <b>Charles Dickens</b> with 30 listed Books. But, to find also <b>Dan Brown</b> on this list tells me, that popularity doesn't hold for literary excellency or quality. At least he is last among the Top 40 after <b>Herman Melville</b>, <b>George Orwell</b>, <b>Daniel Defoe</b>, <b>Lewis Caroll</b> or<b> Jonathan Swift</b>. On the other hand, <b>John Milton</b> did not become rich with his one shot<i> Paradise Lost </i>although it is rather popular.<br />
<br />
Here are the links to the online queries to get the most recent and complete results:<br />
<ul>
<li><a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+%3Fname+COUNT%28DISTINCT%28%3Fwork%29%29+as+%3FnumOfWorks+xsd%3Afloat%28xsd%3Ainteger%28AVG%28%3Findegree%29*10%29%29%2F10+as+%3FpopularityOfWorks%0D%0AWHERE+%7B%0D%0A+++++++%3Fauthor+rdf%3Atype+dbpedia-owl%3AWriter+%3B%0D%0A+++++++++++++++rdfs%3Alabel+%3Fname+FILTER+%28lang%28%3Fname%29%3D%22en%22%29+.%0D%0A+++++++%3Fwork+++dbpedia-owl%3Aauthor+%3Fauthor+%3B%0D%0A+++++++++++++++rdf%3Atype+dbpedia-owl%3ABook+%3B%0D%0A+++++++++++++++dbpedia-owl%3AwikiPageInLinkCount+%3Findegree+.%0D%0A%7D%0D%0AGROUP+BY+%3Fname%0D%0AORDER+BY+DESC%28COUNT%28*%29%29%0D%0A%0D%0A&format=text%2Fhtml&timeout=30000&debug=on">the Top Prolific Authors</a></li>
<li><a href="http://dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+dbpedia-owl%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E%0D%0APREFIX+dcterms%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E%0D%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0A%0D%0ASELECT+%3Fname+COUNT%28DISTINCT%28%3Fwork%29%29+as+%3FnumOfWorks+xsd%3Afloat%28xsd%3Ainteger%28AVG%28%3Findegree%29*10%29%29%2F10+as+%3FpopularityOfWorks%0D%0AWHERE+%7B%0D%0A+++++++%3Fauthor+rdf%3Atype+dbpedia-owl%3AWriter+%3B%0D%0A+++++++++++++++rdfs%3Alabel+%3Fname+FILTER+%28lang%28%3Fname%29%3D%22en%22%29+.%0D%0A+++++++%3Fwork+++dbpedia-owl%3Aauthor+%3Fauthor+%3B%0D%0A+++++++++++++++rdf%3Atype+dbpedia-owl%3ABook+%3B%0D%0A+++++++++++++++dbpedia-owl%3AwikiPageInLinkCount+%3Findegree+.%0D%0A%7D%0D%0AGROUP+BY+%3Fname%0D%0AORDER+BY+DESC%28AVG%28%3Findegree%29%29%0D%0A%0D%0A&format=text%2Fhtml&timeout=30000&debug=on">the Most Popular Authors</a></li>
</ul>
<div>
Enjoy....I'll be back, when I will find again something interesting ;-)</div>
<br />
<br />
<br />Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-59144971947347891542012-08-17T17:39:00.001+02:002012-08-17T17:39:32.131+02:00Who Knows Movies - The 2nd Round<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHzF4K26ySt9F2ssI2tch5dPwzXz4jfpMMD2XeLFi4Kbp02nSILfAqNh9Nu2RVOgvoGSsGEoZstc9ggZnBRjmGZ8ZYjv2qvFnrf56EN0FJ5C4tpA6q28OWwLxZuKFXCgnrhUgybQ/s1600/Bildschirmfoto+2012-06-23+um+14.57.21.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHzF4K26ySt9F2ssI2tch5dPwzXz4jfpMMD2XeLFi4Kbp02nSILfAqNh9Nu2RVOgvoGSsGEoZstc9ggZnBRjmGZ8ZYjv2qvFnrf56EN0FJ5C4tpA6q28OWwLxZuKFXCgnrhUgybQ/s320/Bildschirmfoto+2012-06-23+um+14.57.21.jpg" width="301" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="http://141.89.225.43/whoknowsmovies/game.html">Play it</a></td></tr>
</tbody></table>
We are happy to announce that our paper<br />
<blockquote class="tr_bq">
<i>Andreas Thalhammer, Magnus Knuth and Harald Sack:</i> <i>"Evaluating Entity Summarizations Using a Game-Based Ground Truth" </i></blockquote>
has been accepted for the <a href="http://iswc2012.semanticweb.org/evaluations-and-experiments-track-accepted-papers">Evaluations and Experiments Track of ISWC 2012</a>.
We want to thank all of you very much, who played our WhoKnowsMovies? game, which allowed us to collect the necessary data for this publication. And since we also want provide updated statistics for the final version of our paper, you are very welcome to play a little bit more... :)<br />
<br />
There has been a little upgrade for the game including more questions and we need more data to achieve a more reliable proof of our assumption that our proposed fact ranking for entity summarization really is better than a random choice.<br />
<br />
<b>Therefore, please help us and <a href="http://141.89.225.43/whoknowsmovies/game.html">play the game</a>. Test your knowledge about movies! Can you challenge the highscore? </b><br />
<b><br /></b>
P.S. The already gathered (anonymized) data is available at <a href="http://www.yovisto.com/labs/iswc2012/">http://www.yovisto.com/labs/iswc2012/</a>.<br />
<br />Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-73734000022310129522012-06-23T15:09:00.001+02:002012-06-23T15:50:32.611+02:00Who knows Movies? Another Game with a Purpose for the Semantic Web<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHzF4K26ySt9F2ssI2tch5dPwzXz4jfpMMD2XeLFi4Kbp02nSILfAqNh9Nu2RVOgvoGSsGEoZstc9ggZnBRjmGZ8ZYjv2qvFnrf56EN0FJ5C4tpA6q28OWwLxZuKFXCgnrhUgybQ/s1600/Bildschirmfoto+2012-06-23+um+14.57.21.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHzF4K26ySt9F2ssI2tch5dPwzXz4jfpMMD2XeLFi4Kbp02nSILfAqNh9Nu2RVOgvoGSsGEoZstc9ggZnBRjmGZ8ZYjv2qvFnrf56EN0FJ5C4tpA6q28OWwLxZuKFXCgnrhUgybQ/s320/Bildschirmfoto+2012-06-23+um+14.57.21.jpg" width="301" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="http://141.89.225.43/whoknowsmovies/game.html">Play it</a></td></tr>
</tbody></table>
Of course you will remember our last Quiz Game 'Who Knows' (cf. the post below), where we have used DBpedia facts to generate questionaires for a game with a purpose. The very first application of this game was to put to evaluate some newly developed entity/property heuristics via crowdsourcing. Then, we realized that the gathered data also had some collateral benefits such as the detection of inconsistencies and flaws within the Linked Data resources that we used for generating the questions.<br />
<br />
Now, we are looking into another task: entity summarization. Entity summarization means that we are trying to wrap up only the most important facts that determine a distinct entity. Just think of the Google knowledge graph that displays entity summaries from Freebase. Thus, Entity summarization of course is rather similar to relevance ranking of facts.<br />
<br />
To generate a ground truth for evaluation of entity summarization heuristics, we adapted our quiz game WhoKnows? to become WhoKnows?Movies! We are cooperating with Andreas Thalhammer from University of Innsbruck with this task and we have restricted the domain of questions to popular movies, adapted the gereration of questionaires by utilizing the Freebase knowledge Base. Now we have to gather data....<br />
<br />
<b>Therefore, please help us and <a href="http://141.89.225.43/whoknowsmovies/game.html">play the game</a>. Test your knowledge about movies! Can you challenge the highscore? </b><br />
<b><br /></b><br />
P.S. Of course all the data gathered will be anonymized and made publicly avaible.<br />
<br />Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-46363659585480011112011-06-25T10:30:00.002+02:002011-06-25T10:32:57.849+02:00New Challenge! Playing WhoKnows? to develop a new Teflon Pan<a href="http://apps.facebook.com/whoknows_/" onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgtQzvoDbaidbYn37T33n-mI8aRLTOh1HIL6ltqsmio9uv74HXsrS0KagpQn5LX9qB-OfxDJ8V7YuP3_soHZdGyfzftwHAsO14QWPilQOX52z5PVKwTUrfCD2quvRLs9S3XuHHM0A/s200/whoknowslogo.png" border="0" alt="" id="BLOGGER_PHOTO_ID_5622069358316444642" style="float: left; margin-top: 0px; margin-right: 10px; margin-bottom: 10px; margin-left: 0px; cursor: pointer; width: 200px; height: 200px; " /></a><br />Some of you already may know our --serious-- fun game <b>WhoKnows?</b> (<a href="http://www.hpi.uni-potsdam.de/fileadmin/hpi/FG_ITS/Semantic-Technologies/paper/Ludwig2011a.pdf"><i>N. Ludwig, J. Waitelonis, M. Knuth, H. Sack: WhoKnows? - Evaluating Linked Data Heuristics with a Quiz that Cleans Up DBpedia</i></a>) that has been presented inter alia at this year's ESWC 2011 (cf. <a href="http://twitpic.com/55cbqx">picture from poster session</a>).<div><b><br /></b></div><div><b>WhoKnows?</b> is a quiz game based on the DBpedia dataset; while answering the quickies the player produces data that can be used for the ranking of facts from the underlying knowledge base. Furthermore, the player has the possibility to mark strange questions often originating from inconsistencies that we want to identify this way.<br /><br /><div>Now, as a next step we want to apply the collected data for the <b>development of an expert finder</b> <b>and user interest profile recommendation system</b>. For this we would appreciate a larger data set that allows us to rate the expertise of several users in various domains. If you like to contribute this research, you can do this easily by <b></b><b><a href="http://apps.facebook.com/whoknows_/">playing WhoKnows?</a> on Facebook</b>. In order to make a sound statement about your expertise, we need at least about thirty questions answered and of course the more the better.</div><div><div><b><br /></b></div><div><b>Of course all gathered data will be anonymized before analysis and evaluation</b>.</div><div><br /></div><div>Don't forget it's really fun and educational together!<br />Your help really is appreciated. Thank you <a href="http://apps.facebook.com/whoknows_/">for playing</a>!</div><div><br /></div><div>P.S. We would be pleased to inform you about the final results, if you are interested in. Just send us an e-mail.<br /></div></div></div>Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-43558884295115541352011-02-25T14:40:00.003+01:002011-02-25T14:47:50.860+01:00Mediaglobe - the Digital ArchiveDer neue Teaser-Trailer für unser Projekt 'Mediaglobe - The Digital Archive' ist online. Weitere Infos über das semantische Videosuchmaschinenprojekt unter der <a href="http://www.projekt-mediaglobe.de/">Mediaglobe Projekt-Webseite</a> oder über unsere <a href="http://www.hpi.uni-potsdam.de/meinel/forschung/future_internet/semantic_technologies.html">Semantic Technologies</a> Webseite am HPI.<div><br /><iframe title="YouTube video player" width="560" height="349" src="http://www.youtube.com/embed/QL5dCtpeF1s" frameborder="0" allowfullscreen=""></iframe><br /></div>Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-30565606218406753802010-11-24T09:40:00.004+01:002010-11-24T09:54:48.328+01:00'Who knows?' - A Semantic Web Game<a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="http://apps.facebook.com/whoknows_/"><img style="float:left; margin:0 10px 10px 0;cursor:pointer; cursor:hand;" src="http://141.89.225.43/img/logo.png" width="120" border="0" alt="" /></a><b>Please support our research by playing our Semantic Web Game 'Who Knows'!</b><div><br /></div><div><b>What is 'Who Knows?'</b></div><div>'<a href="http://apps.facebook.com/whoknows_/">Who Knows?</a>' is a simple Q&A Game in the style of 'Who wants to be a Millionaire'. The questions are automatically generated from <a href="http://dbpedia.org/About">DBpedia</a> content.</div><div><br /></div><div><b>What is the purpose of '<a href="http://apps.facebook.com/whoknows_/">Who Knows?</a>'</b></div><div>The purpose is the evaluation of some heuristics that are used to determine a ranking of facts within a knowledge base such as e.g. DBpedia. </div><div><br /></div><div>These are the <b>simple assumptions</b> '<a href="http://apps.facebook.com/whoknows_/">Who Knows?</a>' is based on:</div><div><ol><li>If a user knows the correct answer, the fact seems to be 'important'.</li><li>If a user doesn't know the correct answer, the fact seems to be not so 'important'.</li><li>If a user votes the question to be wrong, odd, or strange, the fact seems to be 'irrelevant'.</li></ol><div>There a <b>different variants</b> to play the game:</div></div><div><ol><li>One-on-One questions -- only one choice is correct.</li><li>N-to-One questions -- there are multiple correct answers.</li><li>Hangman -- find the answer by playing the popular game of hangman.</li><li>Maths -- find the answer and compute a simple arithmetic formula.</li></ol><div>Meanwhile you will receive points for correct answers. The faster you provide the answer, the more points you will get. If you provide the wrong answer, you'll loose a life and some points will be taken from your score. </div><div><br /></div><div><b>Try to score as many points as possible and <a href="http://apps.facebook.com/whoknows_/">don't forget to tell your friends</a>!!!!</b></div></div>Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-85988379207701686492010-09-05T19:25:00.004+02:002010-09-05T22:07:58.850+02:00Schuster bleib bei Deinen Leisten - Stendhal und die Kryptografie<a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1iCNW4lKUXEGtHBW76K68ujY4WXjw8jp3P9xuQci7pQCZa_AliDLmKQn-DHgAeAY5m9Ru2t8tNrBtMsuoiBhyaTxVyfzxJjWq1m2iATDMktaGKN0HIBH5J1DDL53K0hOHYuwvog/s1600/StendhalCharterhouseParma01.jpg"><img style="float:left; margin:0 10px 10px 0;cursor:pointer; cursor:hand;width: 160px; height: 240px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1iCNW4lKUXEGtHBW76K68ujY4WXjw8jp3P9xuQci7pQCZa_AliDLmKQn-DHgAeAY5m9Ru2t8tNrBtMsuoiBhyaTxVyfzxJjWq1m2iATDMktaGKN0HIBH5J1DDL53K0hOHYuwvog/s200/StendhalCharterhouseParma01.jpg" border="0" alt="" id="BLOGGER_PHOTO_ID_5513487516954757618" /></a>Was haben der französische Autor Henri Beyle aka <a href="http://de.wikipedia.org/wiki/Stendhal">Stendhal</a> und Kryptografie miteinander zu tun, wird sich der geneigte Leser fragen. Die Antwort darauf liegt in Stendhals großen Roman 'Die Kartause von Parma', die ich dieses Jahr als Urlaubslektüre ausgewählt hatte (<a href="http://biblionomicon.blogspot.com/2010/08/groes-kino-stendhal-die-kartause-von.html">und hier im biblionomicon rezensiert habe</a>). Darin geht es um den jungen Helden Fabrizio, der aufgrund eines unbeabsichtigten Duells mit Todesfolge in die berüchtigte Zitadelle von Parma gesteckt wird, aus der noch nie jemand entfliehen konnte. Sie erhebt sich als gewaltiger Turm, der Engelsburg in Rom nachempfunden, auf deren Plateau sich Fabrizios Gefängniszelle befindet. Fabrizio liebt Clelia, die Tochter des kommandierenden Generals der Festung, und kommuniziert mit ihr via 'optischer Telegrafie'. Dies allerdings auf denkbar einfachste Weise, indem Seiten aus einem Buch mit jeweils einem großen Buchstaben des Alphabets versehen werden und diese nacheinander an einem Fenster präsentiert werden.<div><br /></div><div>So weit so gut... das hat ja noch nichts mit Verschlüsselung zu tun. Fabrizios Freunde außerhalb der Festung hecken einen Fluchtplan aus und müssen daher mit ihm unerkannt kommunizieren. Dies gelingt ihnen nachts mit Hilfe von Lichtzeichen - also wieder 'optische Telegrafie', die allerdings verschlüsselt werden muss, damit niemand hinter ihre Pläne kommt. In der ersten, noch unverschlüsselten Version, entspricht dabei jeder Buchstabe einer Leuchtzeichenfolge entsprechend seiner Position im Alphabet, also 'a' einmal leuchten, 'b' zweimal leuchten, usw.</div><div><br /></div><div>Für die eigentliche Verschlüsselung wird Fabrizio ein Brief in seine Zelle geschmuggelt. Allerdings enthält dieser nicht nur den Fluchtplan im Klartext sondern auch noch den vollständigen Schlüssel (für eine <a href="http://de.wikipedia.org/wiki/Monoalphabetische_Substitution">einfache Substitutionschiffre</a>) für die zukünftige Lichtzeichenkommunikation. Der Schlüssel alleine wäre doch schon gefährlich genug gewesen. Ich frage mich, wenn schon der Fluchtplan im Detail mitgeteilt wird, wozu braucht es dann noch einen Schlüssel. Würde der Brief kompromitiert werden, wäre alles verloren...</div><div><br /></div><div>Allerdings klärt uns der Anhang des Buches darüber auf, dass Stendhal in seiner Funktion als französischer Konsul von Civitavecchia ein ähnlicher Lapsus im Zuge seiner Amtsgeschäfte unterlaufen wäre. In einem verschlüsselten Brief an den französischen Außenminister fügte er 1835 im Klartext noch den kompletten Schlüssel hinzu und schickte beide Nachrichten gemeinsam in einem Brief. Das ist ein absoluter Anfängerfehler und Stendhal wurde völlig zurecht dafür offiziell vom Außenminister gerügt.</div><div><br /></div><div>Schön ist die Episode aber als literarisches Kryptografiebeispiel, das ich gerne auch in einer meiner Vorlesungen aufgreifen werde. Neben <a href="http://de.wikipedia.org/wiki/Edgar_Allan_Poe">Edgar Allan Poes</a> '<a href="http://www.staff.uni-mainz.de/pommeren/Kryptologie/Klassisch/0_Unterhaltung/Lit/Goldbug.html">Goldkäfer</a>' und <a href="http://de.wikipedia.org/wiki/Arthur_Conan_Doyle">Arthur Conan Doyles</a> Sherlock Holmes Episode '<a href="http://en.wikipedia.org/wiki/The_Adventure_of_the_Dancing_Men">Das Musgrave Ritual</a>', ein weiteres Beispiel für den Einsatz von Kryptografie, um die Spannung in einem Roman zu erhöhen.</div>Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-42045632485721197412010-07-27T15:10:00.011+02:002010-07-27T22:07:11.868+02:00There are more Things in Heaven and Earth... - DBPedia Link Graph Analysis Revisited<a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj2jrNURl5jYjoe1TyUGZWyFbgOqVbC3Kdv2COBjb5kLxYSHWQRl-K0bTSqYNG2nM5h-1HgWmvxK5Ew0ft7WINnLWfI_CLNtN8l1Zbd9npHo-nVCKvSHPcih_BiBg8iniRzsTTkSA/s1600/sherlock.gif"><img style="float:left; margin:0 10px 10px 0;cursor:pointer; cursor:hand;width: 82px; height: 100px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj2jrNURl5jYjoe1TyUGZWyFbgOqVbC3Kdv2COBjb5kLxYSHWQRl-K0bTSqYNG2nM5h-1HgWmvxK5Ew0ft7WINnLWfI_CLNtN8l1Zbd9npHo-nVCKvSHPcih_BiBg8iniRzsTTkSA/s200/sherlock.gif" border="0" alt="" id="BLOGGER_PHOTO_ID_5498583717827144770" /></a>In the course of our ongoing work with Linked Open Data, we recently made some analysis on the graph structure of <a href="http://dbpedia.org/">DBPedia</a> data. For this we only took under consideration the original link graph (aka '<a href="http://wiki.dbpedia.org/Downloads351#pagelinks">wikilinks</a>'), where we did some cleanup first, such as, e.g., resolving redirects, etc.<br /><br /><div>As a side effect, we had to compute in-degree and out-degree of all DBPedia entities according to wikilinks, ... and we discovered some more or less surprising facts (thanks to Nadine):</div><div><br /></div><div>The entity with the hightest out-degree (i.e. number of outgoing links) currently is:</div><div><a href="http://dbpedia.org/resource/List_of_places_in_Afghanistan">http://dbpedia.org/resource/List_of_places_in_Afghanistan</a><br /><div>with 7.147 outlinks (<i>after cleanup, and remember it's wikilinks and not typed links of DBPedia</i>)</div></div><div><br /></div><div>The entity with the highest in-degree (i.e. number of incoming links) currently is:</div><div><a href="http://dbpedia.org/resource/Living_people">http://dbpedia.org/resource/Living_people</a></div><div>with 440.151 inlinks (<i>after cleanup</i>)</div><div><br /></div><div>While the 2nd one (living people) seemed pretty clear to me, the first (Afghanistan places...) was a bit of a surprise (as also are trilobytes...). For all the explorers among us, I have included the Top Ten list of incoming and outgoing wikilinks, each with indegree and associated outdegree...</div><div><br /></div><div><div><br /></div><table border="1"><tbody><tr><th align="left"><span class="Apple-style-span" style="font-size:small;"> Top Ten Incoming</span></th> <th align="right"><span class="Apple-style-span" style="font-size:small;">in</span></th> <th align="right"><span class="Apple-style-span" style="font-size:small;">out</span></th></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/Living_people</span></td> <td><span class="Apple-style-span" style="font-size:small;"><b> 440151</b></span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 0</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/United_States</span></td> <td><span class="Apple-style-span" style="font-size:small;"><b> 385407</b></span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 963</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/France</span></td> <td><span class="Apple-style-span" style="font-size:small;"><b> 124206</b></span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 759</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/England </span></td> <td><span class="Apple-style-span" style="font-size:small;"><b>123223</b></span></td> <td><span class="Apple-style-span" style="font-size:small;"> 1320</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/United_Kingdom</span></td> <td><span class="Apple-style-span" style="font-size:small;"><b> 121203</b></span></td> <td><span class="Apple-style-span" style="font-size:small;"> 1152</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/List_of_sovereign_states</span></td> <td><span class="Apple-style-span" style="font-size:small;"><b> 114086 </b></span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;">465</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/Canada</span></td> <td><span class="Apple-style-span" style="font-size:small;"><b> 105849</b></span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 523</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/Germany</span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"><b> 103382</b></span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 889</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/Animal</span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"><b>98680 </b></span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;">236</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/World_War_II</span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"><b> 93555</b></span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 771</span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;"> http://dbpedia.org/resource/Association_football</span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"><b> 90673</b></span></td> <td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 196</span></td></tr></tbody></table></div><br /><br /><span class="Apple-style-span" style="font-size:small;"><br /></span><table border="1"><tbody><tr><th align="left"><span class="Apple-style-span" style="font-size:small;">Top Ten Outgoing</span></th><th aligh="right"><span class="Apple-style-span" style="font-size:small;">in</span></th><th aligh="right"><span class="Apple-style-span" style="font-size:small;">out</span></th></tr><tr><td><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/list_of_places_in_afghanistan</span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;">9</span></td><td><span class="Apple-style-span" style="font-size:small;"><b>7147</b></span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/Flora_of_New_South_Wales</span></td><td><span class="Apple-style-span" style="font-size:small;"> 917 </span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"><b>6819</b></span></td></tr><tr><td style="text-align: left;"><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/List_of_municipalities_of_Brazil</span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 1</span></td><td><span class="Apple-style-span" style="font-size:small;"><b> 5503</b></span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/Index_of_India-related_articles</span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 4 </span></td><td><span class="Apple-style-span" style="font-size:small;"><b>5369</b></span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/Area_codes_in_Germany</span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 6 </span></td><td><span class="Apple-style-span" style="font-size:small;"><b>5360</b></span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/IUCN_Red_List_vulnerable_species_%28Plantae%29</span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 0 </span></td><td><span class="Apple-style-span" style="font-size:small;"><b>5172</b></span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/List_of_trilobites</span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;">6 </span></td><td><span class="Apple-style-span" style="font-size:small;"><b>5102</b></span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/List_of_Social_Democratic_Party_of_Germany_members</span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 24 </span></td><td><span class="Apple-style-span" style="font-size:small;"><b>5078</b></span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/List_of_French_words_of_Germanic_origin</span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;"> 9 </span></td><td><span class="Apple-style-span" style="font-size:small;"><b>5010</b></span></td></tr><tr><td><span class="Apple-style-span" style="font-size:small;">http://dbpedia.org/resource/Index_of_Thailand-related_articles </span></td><td style="text-align: right;"><span class="Apple-style-span" style="font-size:small;">4</span></td><td><span class="Apple-style-span" style="font-size:small;"><b> 4831</b></span></td></tr></tbody></table><br /><div>But there are more interesting things to discover ... stay tuned!</div>Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-91996016057533467202010-07-20T10:24:00.005+02:002010-07-20T13:24:09.082+02:00Visualizing video archive content -- arte.tv<a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgA6mx3VzZ2mA8N9EZSMEYzBeW5ZCo1x4O2sSLr7E5tTGpis3RtH-s116vME4szGw3N-hG8EYNRiiZ82JBYGBHMQWEdaHYrc3gr2aVOqohAEsAFZ-197iYNkLrFxBs9KAPN_E-ADQ/s1600/arte.jpg"><img style="float:left; margin:0 10px 10px 0;cursor:pointer; cursor:hand;width: 200px; height: 189px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgA6mx3VzZ2mA8N9EZSMEYzBeW5ZCo1x4O2sSLr7E5tTGpis3RtH-s116vME4szGw3N-hG8EYNRiiZ82JBYGBHMQWEdaHYrc3gr2aVOqohAEsAFZ-197iYNkLrFxBs9KAPN_E-ADQ/s200/arte.jpg" border="0" alt="" id="BLOGGER_PHOTO_ID_5495907140816549314" /></a>Okay, first at all, it's been a while that I have written a blog post here. I guess that's some tribute to the ever faster spinning world of digital media as I had concentrated more on shorter and therefore, faster means of communication such as <a href="http://twitter.com/lysander07">twitter</a> and (shame on me...) <a href="http://www.facebook.com/lysander07">facebook</a>. Nevertheless, while skipping through the pages of <a href="http://moresemantic.blogspot.com/">moresemantic</a> I decided to revive the blog and to keep on posting about current research work....<div><br /></div><div>This morning, I had to look up a documentary '<a href="http://www.arte.tv/de/3320934.html">The Digital Bomb</a>', which had been broadcasted yesterday evening on the German/French <a href="http://www.arte.tv/">arte television channel</a>. Arte is one of the public service television channels focussing on culture and arts. As many other television broadcasters, arte of course maintains a website and being as a television broadcaster there is also some sort of media archive. As being a public service television broadcaster, some strange regulations keep the archive from maintaining more than 7 days of tv-program -- but this is <a href="http://en.wikipedia.org/wiki/And_Now_for_Something_Completely_Different">something completely different</a> (as to speak with Monty Python). This morning, I made some discoveries in <a href="http://videos.arte.tv/de/videos#/fr/videowall/date//1/50/">arte's tv archive</a>, esp. about their way of visualizing content.</div><div><br /></div><div>Besides being a little bit difficult to find the right mode of access -- esp. if you are looking for a specific date of broadcast, I succeeded in finding this nice portal page showing the most <a href="http://videos.arte.tv/de/videos#/fr/videowall/date//1/50/">featured videos of yesterday's tv program</a> including a timeline (at the top) for growing back (and forth) in time. I really like the 2-D tile pattern relating the size of the videos (represented by some significant key frame) to their popularity (or any other ranking). When you place the mouse pointer over a frame you will get more detailed information about the video and by clicking on it the video opens for reviewing.</div><div><br /></div><div>All in all it seems to be inspired by the <a href="http://www.ted.com/themes">TED video archive</a> and there's still room for improvement. I would like to see also timelines for shown content (not only broadcast or production date) as well as geographical information about production/content shown in interactive maps. </div><div><br /></div><div>Now I have become curious about what else is out there? Any new innovative, interactive visualizations for displaying video archive content aside from the youtube mainstream??<br /><div><br /></div></div>Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-71177344281811956182009-10-26T15:01:00.004+01:002009-10-26T15:10:05.019+01:00Open PhD Positions in Semantic Multimedia Retrieval Project<span class="Apple-style-span" style="font-family:Helvetica;"><span class="Apple-style-span" style="font-size: small;"><b>OPEN Ph.D. POSITIONS at Hasso-Plattner-Institute (HPI), Potsdam (Germany) starting on the fourth quarter of 2009<br /></b><br />Hasso-Plattner-Institute (HPI) is a privately financed institute affiliated with the University of Potsdam, Germany. The Institute's founder and benefactor Professor Hasso Plattner, who is also co-founder and chairman of the supervisory board of SAP AG, has created an opportunity for students to experience a unique education in IT systems engineering in a professional research environment with a strong practice orientation.</span></span><div><span class="Apple-style-span" style=" ;font-family:Helvetica;font-size:medium;"><span class="Apple-style-span" style="font-size: small;">(for more information on HPI, c.f. </span><a href="http://www.hpi.uni-potsdam.de/"><span class="Apple-style-span" style="font-size: small;">http://www.hpi.uni-potsdam.de/</span></a><span class="Apple-style-span" style="font-size: small;"> )<br /><br /></span><b><span class="Apple-style-span" style="font-size: small;">Project Description:<br /></span></b><span class="Apple-style-span" style="font-size: small;">MEDIAGLOBE is part of the THESEUS research program initiated by the German Federal Ministry of Economy and Technology (BMWi), with the goal of developing a new Internet-based infrastructure in order to better use and utilize the knowledge available on the Internet. The focus of the research program is on semantic technologies, which determine contents (words, images, sounds, and videos) not through conventional methods (e.g., combinations of letters) but which are able to recognize and place the meaning of a content in its proper context. MEDIAGLOBE deals with digitalization, analysis, and semantic retrieval of historical, documentary audiovisual content. (for more information on MEDIAGLOBE, c.f. </span><a href="http://theseus-programm.de/theseus-mittelstand-2009/"><span class="Apple-style-span" style="font-size: small;">http://theseus-programm.de/theseus-mittelstand-2009/</span></a><span class="Apple-style-span" style="font-size: small;"> )<br /><br />The ideal candidate holds a MS degree in Computer Science or related field and is able to consider both theoretical and practical/implementation aspects in her/his work. Fluent english communication and programming skills are fundamental requirements. Since we are working on a multimedia repository with resources in German language, German language skills are welcome! Preferably the candidate has a background in one of the following<br />fields:<br /></span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">• semantic web technologies<br /></span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">• knowledge representations and ontology engineering<br /></span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">• audiovisual retrieval and analysis<br /></span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">• semantic search<br /></span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">• innovative web development<br /></span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">• user interface design for audiovisual content<br /><br />The position starts as soon as possible and is full-time (40h/week) for the duration of the project until Oct 2011. Review of applications will begin immediately and will continue until the position is filled. The successful candidate will tightly work with international partners and has the possibility to pursue PhD work within the scope of the project.<br /><br /></span><b><span class="Apple-style-span" style="font-size: small;">How to apply:<br /></span></b><span class="Apple-style-span" style="font-size: small;">Excellent candidates are invited to apply with:<br /></span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">• Curriculum vitae and copies of degree certificates/transcripts,<br /></span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">• Writing samples/copies of relevant scientific papers (e.g. thesis, etc.),<br /></span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">• Letters of recommendation.<br /><br />Please send your application in PDF format indicating in the subject 'Application for PhD position‘ via email or via traditional mail to the following contact.<br /><br /></span><b><span class="Apple-style-span" style="font-size: small;">Contact and application:</span><br /></b><span class="Apple-style-span" style="font-size: small;">Harald Sack<br />Hasso-Plattner-Institut für Softwaresystemtechnik GmbH<br />Universität Potsdam<br />Prof.-Dr.-Helmert-Str. 2-3<br />D-14482 Potsdam, Germany<br />phone: +49 (0)331-5509-527<br />fax: </span><span class="Apple-tab-span" style="white-space: pre; "><span class="Apple-style-span" style="font-size: small;"> </span></span><span class="Apple-style-span" style="font-size: small;">+49 (0)331-5509-325<br />email: </span><a href="mailto:harald.sack@hpi.uni-potsdam.de"><span class="Apple-style-span" style="font-size: small;">harald.sack@hpi.uni-potsdam.de</span></a><span class="Apple-style-span" style="font-size: small;"><br />web: </span><a href="http://www.hpi.uni-potsdam.de/meinel/persons/sack.html"><span class="Apple-style-span" style="font-size: small;">http://www.hpi.uni-potsdam.de/meinel/persons/sack.html</span></a></span></div>Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.comtag:blogger.com,1999:blog-17174674.post-1803618907698426832009-10-15T10:09:00.038+02:002009-10-15T17:44:39.420+02:00Opening of the German/Austrian W3C-Office / Teaching the Web at FH Potsdam<a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5Wtoof77-P7DreXQ9j-9c0qddvHze_V4ZZ7u16ltgOTU1L4K_aL2VkpQqmKBs3PnHDEA4zRdDmBvt60XHOkKMxZ_pBLQEGOVoyvwrwLIgjhLGUEjbus_0KGlFbBgR_eKmp_fxPQ/s1600-h/w3coffice.jpg"><img style="float:left; margin:0 10px 10px 0;cursor:pointer; cursor:hand;width: 200px; height: 136px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5Wtoof77-P7DreXQ9j-9c0qddvHze_V4ZZ7u16ltgOTU1L4K_aL2VkpQqmKBs3PnHDEA4zRdDmBvt60XHOkKMxZ_pBLQEGOVoyvwrwLIgjhLGUEjbus_0KGlFbBgR_eKmp_fxPQ/s200/w3coffice.jpg" border="0" alt="" id="BLOGGER_PHOTO_ID_5392741045214354770" /></a><b>Today, I'm visiting the Opening of the German/Austrian W3C-Office at FH Potsdam (only 20 minutes away from HPI with public transportation), which is entitled "</b><a href="http://www.w3c.de/Events/2009/office-opening.html"><b>Teaching the Web</b></a><b>". </b><br /><br />Short Opening address by Prof. Johannes Vielhaber, rector of FH Potsdam and by Felix Sasaki, followed by the first speaker.<br /><a href="http://www.w3.org/People/Klaus/"><b>Klaus Birkenbihl</b></a><b> on "</b><a href="http://www.w3.org/2009/Talks/1015Potsdam-KB/"><b>W3C and W3C Offices - an Overview</b></a><b>"</b>, who gives some general information about W3C and some overview about the worldwide W3C offices and their duties. Today, there are 18 W3C offices and on of their main tasks is the recruiting of local stakeholders to become W3C (paying) members. But also have to be mentioned better synergies with local hosts for community building, acquisition of local projects, and fostering new cooperations.<br /><br /><span style="font-weight:bold;"><a href="http://web.arch.usyd.edu.au/~andrew/">Andrew Vande Moere</a></span> from the University of Sidney on "<span style="font-weight:bold;">Visualization for the Web</span>", author of the <a href="http://infosthetics.com/">information aesthetics blog</a>. He starts with giving an overview on visualization technology ranging from simple data graphs to information art (e.g. the <a href="http://www.baekdal.com/web2dna/">Web2DNA website</a> for visualizing your website as DNA-Sequence...). The main message....ok, open up your data and make your data publicly available (that's my point! Go one step further and make it <a href="http://linkeddata.org/">Linked Open Data</a>!). Then, there are lots of new possibilities for intelligent data mashups (as e.g. the '<a href="http://urbansensing.ning.com/">the city is the future web</a>'), making use of the data in a completely new way. Two interesting examples for web online visualizations are <a href="http://code.google.com/apis/visualization/">Google's visualization API</a> and <a href="http://tinyurl.com/65k5vg">IBM's Many Eyes</a> ('the youtube of visualization'), not to forget tha ultimate data mining &visualization application on your personal data (only for 'the data addicted'), <a href="http://your.flowingdata.com/">http://your.flowingdata.com/</a>.<br /><br /><span style="font-weight:bold;"><a href="http://page.mi.fu-berlin.de/mochol/">Malgorzata Machol</a></span> from FU Berlin (instead of the announced Prof. Tolksdorf...) on "Why Semantic Web " and the "<a href="http://page.mi.fu-berlin.de/mochol/">Semantic Technology Institute</a>". Sorry, but I can't stand those web2.0/3.0/xxx timelines any more. I've seen better motivations for the Semantic Web (at least with better/newer examples). Unfortunately, in this talk the Semantic web is motivated with a rather coarse conception of 'semantics' and (human/machine) 'understanding'. There is too much of human perception (and complex human understanding) mixed up with formal semantics and technology working on formal semantics (and what can be achieved with it w.r.t where are its limits). Please try to achieve more and better differentiation. The second part of the talk is about the STI in Germany and its activities around the semantic web.<br /><br />After the lunch break, the event continues with <a href="http://wikify.org/"><span style="font-weight:bold;">Lambert Heller</span></a> from <a href="http://www.tib.uni-hannover.de/">TIB/UB Hannover</a> on <span style="font-weight:bold;">'Library 2.0 – how the web has (and is) changing education of librarians?</span>'. Today, the library catalogue is nothing but highly structured data, but the problem of uniquely identifying strings (symbols) with subjects (catalogue entries) is only solved on the surface and in a superficial way. There are commonly used (and elaborated) data sources such as the 'Schlagwortnormdatei' or the 'Personennormdatei' that contain reliable structured information about persons (authors) or keywords. But, today these datasets are not publicly available (while it is also not certain, who really owns the copyright, and what kind of copyright at all...). Let's get all these data, triplify it and make it <a href="http://linkeddata.org/">Linked Open Data</a>!!!<br />Finally, come and visit <a href="http://bibcamp.wordpress.com/">3rd BibCamp in Hannover in May 2010</a>:<br /><a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="http://bibcamp.files.wordpress.com/2009/10/bibcamp_header2.jpg"><img style="margin:0 10px 10px 0;cursor:pointer; cursor:hand;width: 770px; height: 140px;" src="http://bibcamp.files.wordpress.com/2009/10/bibcamp_header2.jpg" border="0" alt="" /></a><br /><br /><span style="font-weight:bold;"><a href="http://my.opera.com/patrickhlauke/about/">Patrick H. Lauke</a></span> from (web evangelist at) Opera Software on '<span style="font-weight:bold;">Standards education - what students need to know about web standards and accessibility</span>'. It', about telling the big picture to the students. Not really the complete design specification or code implementation. ('Standards are Code....and Designers don't care about code') Make clear why...not necessarely how. On the other hand, there's accessibility. Web accessibility is more than blind users and screen readers. accessibility doesn't have to be hard. In fact, a lot of accessibility is just usability! Opera accordingly is invoved in<a href="http://www.opera.com/company/education/"> outreach and educational activities on 'Teaching the Web' </a>(Opera: We want to make the Web a better place!).<br /><br /><span style="font-weight:bold;">Petra Rauschenbach</span> from Bundesarchiv on '<span style="font-weight:bold;">Conversion, Digitisation and Internet Gateways. Strategies and their implementation at the Federal Archives of Germany</span>'. Starting with an overview on overview of their stocks and a lot of German archival vocabulary, which I cannot translate into English. Unfortunately, Mrs. Rauschenbach is 'reading' the talk and not talking freely...Also the term '<a href="http://wiki-de.genealogy.net/Findbuch">Findbuch</a>' (i.e. concordance, index) sounds somehow very 'retro' for computer scientiest like me. But anyhow, the content of the Bundesarchiv (Federal Archive) is available on the web....why not make it Linked Open Data??<br /><br /><span style="font-weight:bold;"><a href="http://www.ltg.ed.ac.uk/~ht/">Henry S. Thompson</a></span> from University of Edinburgh on '<span style="font-weight:bold;">Teaching Web Architecture</span>'.<br />students:WWW::fish:water i.e. the relationship between students and the WWW is similar to the relationship of fish and water. So, why teaching a fish about the water? Learn to think about something which usually is invisible (i.e. the technology we are using everyday). So how should we teach web standards? Basic didactic principles like 'Analysis' (decomposing concepts), Contradiction (to something taken for granted), Analogies (being offered for confounding expectations) are presented. I like the last one: E.g. 'Standards come from official standards bodies', but consider the following: IEEE (semi-private bodies) / IETF (bunch of volunteers, left-overs of a hippie community) / W3C (2 private Universities, one semi-private body and companies paying some fee)...all of them have NO legal authority. More on this can also be here: '<a href="http://www.ltg.ed.ac.uk/~ht/eSI_URIs.html">Identity, URIs and Semantic Web</a>'.<br />Conclusion: Teaching needs to draw on theatre as well as educational theory, 'Keeping people engaged is the core of education.'<br /><br /><a href="http://meiert.com/">Jens Meiert</a> from Google Inc. on 'Modern Web development – a view on the future of HTML, CSS and development practices'. As expected, we are starting in the past, 1990 HTML 1.0 by Tim Berners-Lee. What about development practices? Problems in the past ranged from technological limitations, support limitations over low output quality up to bad user experience. In the Present, more and more we are facing a separation between behavior, structure, and presentation.<br /><br /><a href="http://dret.net/netdret/"><span style="font-weight:bold;">Erik Wilde</span></a> from School of Information, UC Berkeley on 'Information Engineering' (BTW, my <a href="http://tinyurl.com/yg6lzax">very first on lecture on web technology</a> back at FSU Jena was based on Erik Wilde's book, this was before I had written <a href="http://www.hpi.uni-potsdam.de/meinel/team/sack/www.html">my own</a> ;-). He's using <a href="http://www.google.com/sidewiki/intl/en/index.html">Google sidewiki</a> for his lecture handouts and presentations, which can be annotated by the students simply by installing the Google toolbar in your browser.<br />Information Engineering is bigger than the web. It includes high level skills such as information and service modelling, or knowledge about complementary architectures. The Web of Things is nothing but applied Information Engineering. Engineering can be defined as constrained-based design and implementation.<br /><br />[to be continued]Biblionomiconhttp://www.blogger.com/profile/04536349145605630326noreply@blogger.com