Term Dependence on the Semantic Web

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Presentation given at ISWC2008. It analyzes complex network characteristics of dependence between terms (i.e. classes and properties) on the Semantic Web as well as dependence between Web ontologies.

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Term Dependence on the Semantic Web

  1. 1. Term Dependence on the Semantic Web Gong Cheng and Yuzhong Qu Institute of Web Science (IWS) Southeast University, P.R. China at ISWC 2008, Karlsruhe, Germany
  2. 2. What is happening on the hypertext Web
  3. 3. How about the Semantic Web foaf:Person foaf:Agent wn:Agent-3 rdfs:subClassOf rdfs:subClassOf
  4. 4. Term Dependence Graph (TDG) foaf:Person foaf:Agent rdfs:subClassOf wn:Agent-3
  5. 5. Data Set <ul><li>As of April 2008 </li></ul><ul><ul><li>9.8 million well-formed RDF documents </li></ul></ul><ul><ul><li>114,408 hosts, or 7,290 registered domain names </li></ul></ul><ul><ul><li>400 million RDF triples </li></ul></ul>http://iws.seu.edu.cn/services/falcons/
  6. 6. Distribution of RDF documents on websites bio2rdf.org dbpedia.org openlinksw.com buzznet.com bibsonomy.org l3s.de …
  7. 7. Size distribution of RDF documents Maximum at 5 NCI Thesaurus WordNet
  8. 8. How large is the TDG analyzed <ul><li>1,278,233 terms as nodes (from 3,039 vocabularies) </li></ul><ul><ul><li>1,158,480 classes (90.6%) </li></ul></ul><ul><ul><li>118,808 properties (9.3%) </li></ul></ul><ul><ul><li>945 both (0.1%) </li></ul></ul><ul><li>7,312,657 arcs (after removing self-loops) </li></ul>
  9. 9. Distribution of terms in vocabularies EthanAnimals OpenCyc DBpedia properties EthanAnimals FMA
  10. 10. Degree analysis of TDG <ul><li>In-degree: direct influence degree </li></ul><ul><li>Out-degree: direct dependence degree </li></ul><ul><li>Average in-/out-degree: 5.72 </li></ul>
  11. 11. In-degree rdf:type rdfs:subClassOf owl:Class rdfs:label rdfs:comment rdfs:Class owl:equivalentClass … cyc:guid … 64.9%
  12. 12. Out-degree 40.9% at 5 Focal classes
  13. 13. Correlation <ul><li>Pearson’s correlation coefficient between in-degrees and out-degrees </li></ul><ul><ul><li>0.006 ∈ [-1, 1] </li></ul></ul>
  14. 14. Distance analysis of TDG foaf:Person foaf:Agent rdfs:subClassOf wn:Agent-3
  15. 15. Dependence depth Average dependence depth: 10 Leaf classes in FMA
  16. 16. Hop plot and effective diameter
  17. 17. Connectivity analysis of TDG 93.4% in FMA
  18. 18. Connectivity analysis of TDG over 40,000 disconnected pieces after 16 nodes are removed
  19. 19. Vocabulary Dependence Graph (VDG) foaf:Person foaf:Agent rdfs:subClassOf wn:Agent-3 foaf rdfs wn
  20. 20. How large is the VDG analyzed <ul><li>3,039 vocabularies as nodes </li></ul><ul><li>11,392 arcs (after removing self-loops) </li></ul>
  21. 21. Degree analysis of VDG rdf, rdfs, owl, daml Average in-/out-degree: 3.75
  22. 22. Distance analysis of VDG
  23. 23. Connectivity analysis of VDG
  24. 24. Main results <ul><li>Power-laws </li></ul><ul><li>Complex structures within ontologies </li></ul><ul><ul><li>long distance between terms </li></ul></ul><ul><li>Only a few links between ontologies (excluding language-level ontologies) </li></ul><ul><li>All the data are available online. </li></ul>
  25. 25. More words <ul><li>Linking Open Data (LOD) </li></ul><ul><li>Linking Ontologies on the Web (LOW) </li></ul><ul><ul><li>third-party links, e.g. ontology matching </li></ul></ul><ul><ul><li>publishing links online (collaboratively) </li></ul></ul>
  26. 26. Thank you for your attention yu`yiwon 语义网 Semantic Web shu`shin 属性 Property ley 类 Class benty 本体 Ontology P ronunciation Chinese English
  27. 27. Power-law

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