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01/06/15Sergey Chernov 1 Extracting Semantic Relationships between Wikipedia Categories By Sergey Chernov, Tereza Iofciu, Wolfgang Nejdl, Xuan Zhou, Michal.

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Presentation on theme: "01/06/15Sergey Chernov 1 Extracting Semantic Relationships between Wikipedia Categories By Sergey Chernov, Tereza Iofciu, Wolfgang Nejdl, Xuan Zhou, Michal."— Presentation transcript:

1 01/06/15Sergey Chernov 1 Extracting Semantic Relationships between Wikipedia Categories By Sergey Chernov, Tereza Iofciu, Wolfgang Nejdl, Xuan Zhou, Michal Kopycki, Przemyslaw Rys

2 01/06/15Sergey Chernov 2 Preliminaries  WIKIPEDIA: largest knowledge sharing system  Many pages assigned to CATEGORIES  All links are NAVIGATIONAL  Can we extract SEMANTIC links? MOTIVATION

3 01/06/15Sergey Chernov 3 Wikipedia Categories Example MOTIVATION

4 01/06/15Sergey Chernov 4 Possible benefits  Semi-structured queries “find Countries which had Democratic Non-Violent Revolutions” rephrased as “find page from category Countries which is connected to some page in Non-Violent Revolutions”  Hints for authors “you edit page from category Countries, do you want to add a link to page in category Capital?”  Raw data for manual semantic markup MOTIVATION

5 01/06/15Sergey Chernov 5 Countries Heuristics Experiments Denmark Austria Capitals Berlin Stockholm Vienna Germany France Paris  Number of links NL = 3  Connectivity Ratio CR = 3/4 = 0.75

6 01/06/15Sergey Chernov 6 Dataset  INEX 2006 collection  Sample category rankings Experiments

7 01/06/15Sergey Chernov 7 Manual assessment methodology  Semantic Connection Strength (SCS) Measure:  2 = strong semantic relationship,  1 = average semantic relationship,  0 = weak or no semantic relationship.  Instruction for Assessors  “category A is strongly related to category B (value 2) if you believe that every page in A should conceptually have at least one semantic link to B;”  “A and B are averagely related (value 1), if you believe 50% of pages in A should have semantic links to B;”  “otherwise, A and B are weakly related (value 0).”

8 01/06/15Sergey Chernov 8 Experiments with Number of Links Average semantic connections strength for 100 sample categories, extracted using Number of Links. Experiments

9 01/06/15Sergey Chernov 9 Experiments with Connectivity Ratio Average semantic connections strength for 100 sample categories, extracted using Connectivity Ratio. Experiments

10 01/06/15Sergey Chernov 10 General Results and Conclusions  Result is skewed toward Countries category  Connectivity Ratio is a better measure than Number of Links  We have observed that inlinks have better performance than outlinks. Summary

11 01/06/15Sergey Chernov 11 Future Steps More manual exploration, look for additional heuristics Consider more categories SCS composed of  Is this a “part of” relation? W1  Is this a “is a” relation? W2  Is this a “synonym” relation? W3  Is this a “antonym” relation? W4  It is related in a different way? Which one? W5 Summary

12 01/06/15Sergey Chernov 12 Thank You!


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