Catriple: Extracting Triples from Wikipedia Categories

  • Qiaoling Liu
  • Kaifeng Xu
  • Lei Zhang
  • Haofen Wang
  • Yong Yu
  • Yue Pan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5367)

Abstract

As an important step towards bootstrapping the Semantic Web, many efforts have been made to extract triples from Wikipedia because of its wide coverage, good organization and rich knowledge. One kind of important triples is about Wikipedia articles and their non-isa properties, e.g. (Beijing, country, China). Previous work has tried to extract such triples from Wikipedia infoboxes, article text and categories. The infobox-based and text-based extraction methods depend on the infoboxes and suffer from a low article coverage. In contrast, the category-based extraction methods exploit the widespread categories. However, they rely on predefined properties, which is too effort-consuming and explores only very limited knowledge in the categories. This paper automatically extracts properties and triples from the less explored Wikipedia categories so as to achieve a wider article coverage with less manual effort. We manage to realize this goal by utilizing the syntax and semantics brought by super-sub category pairs in Wikipedia. Our prototype implementation outputs about 10M triples with a 12-level confidence ranging from 47.0% to 96.4%, which cover 78.2% of Wikipedia articles. Among them, 1.27M triples have confidence of 96.4%. Applications can on demand use the triples with suitable confidence.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: A nucleus for a web of open data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Auer, S., Lehmann, J.: What have innsbruck and leipzig in common? Extracting semantics from wiki content. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 503–517. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Herbelot, A., Copestake, A.: Acquiring ontological relationships from wikipedia using RMRS. In: Proc.of the ISWC 2006 Workshop on Web Content Mining with Human Language Technologies (2006)Google Scholar
  7. 7.
    Nguyen, D.P.T., Matsuo, Y., Ishizuka, M.: Exploiting Syntactic and Semantic Information for Relation Extraction from Wikipedia. In: IJCAI Workshop on Text-Mining & Link-Analysis, TextLink 2007 (2007)Google Scholar
  8. 8.
    Ponzetto, S.P., Strube, M.: Deriving a large-scale taxonomy from wikipedia. In: AAAI 2007, pp. 1440–1445 (2007)Google Scholar
  9. 9.
    Ponzetto, S.P., Strube, M.: Knowledge derived from wikipedia for computing semantic relatedness. Journal of Artificial Intelligence Research 30, 181–212 (2007)Google Scholar
  10. 10.
    Strube, M., Ponzetto, S.P.: Wikirelate! computing semantic relatedness using wikipedia. In: AAAI (2006)Google Scholar
  11. 11.
    Suchanek, F., Kasneci, G., Weikum, G.: Yago: A large ontology from wikipedia and wordnet. Research Report MPI-I-2007-5-003, Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany (2007)Google Scholar
  12. 12.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: WWW (2007)Google Scholar
  13. 13.
    Wang, G., Yu, Y., Zhu, H.: Pore: Positive-only relation extraction from wikipedia text. In: ISWC/ASWC, pp. 580–594 (2007)Google Scholar
  14. 14.
    Wu, F., Weld, D.S.: Autonomously semantifying wikipedia. In: CIKM (2007)Google Scholar
  15. 15.
    Wu, F., Weld, D.S.: Automatically refining the wikipedia infobox ontology. In: WWW, pp. 635–644 (2008)Google Scholar
  16. 16.
    Yu, J., Thom, J.A., Tam, A.M.: Ontology evaluation using wikipedia categories for browsing. In: CIKM, pp. 223–232 (2007)Google Scholar
  17. 17.
    Zesch, T., Gurevych, I.: Analysis of the wikipedia category graph for nlp applications. In: Proceedings of the TextGraphs-2 Workshop (NAACL-HLT) (2007)Google Scholar
  18. 18.
    Zirn, C., Nastase, V., Strube, M.: Distinguishing between instances and classes in the wikipedia taxonomy. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 376–387. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Qiaoling Liu
    • 1
  • Kaifeng Xu
    • 1
  • Lei Zhang
    • 2
  • Haofen Wang
    • 1
  • Yong Yu
    • 1
  • Yue Pan
    • 2
  1. 1.Apex Data and Knowledge Management LabShanghai Jiao Tong UniversityShanghaiChina
  2. 2.IBM China Research LabBeijingChina

Personalised recommendations