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Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring

  • Linyun Fu
  • Haofen Wang
  • Haiping Zhu
  • Huajie Zhang
  • Yang Wang
  • Yong Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4825)

Abstract

Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintaining such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel difficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an integrated solution to make Wikipedia authoring easier based on RDF graph matching, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and auto-completes internal links between Wikipedia articles for the user; 2) a category suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source.

Keywords

Graph Match Query Graph Blank Node Resource Graph Prefix Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Ruiz-Casado, M., Alfonseca, E., Castells, P.: From Wikipedia to Semantic Relationships: a semi-automated Annotation Approach. ESWC (2006)Google Scholar
  2. 2.
    Hepp, M., Bachlechner, D., Siorpaes, K.: Harvesting Wiki Consensus - Using Wikipedia Entries as Ontology Elements. SemWiki (2006)Google Scholar
  3. 3.
    Chernov, S., Iofciu, T., Nejdl, W., Zhou, X.: Extracting Semantic Relationships between Wikipedia Categories (2006)Google Scholar
  4. 4.
    Krötzsch, M., Vrandečić, D., Völkel, M.: Wikipedia and the Semantic Web: the Missing Links (2005)Google Scholar
  5. 5.
    Voss, J.: Collaborative thesaurus tagging the Wikipedia way. Wikimetrics (2006)Google Scholar
  6. 6.
    Adafre, S.F., de Rijke, M.: Discovering Missing Links in Wikipedia. LinkKDD (2005)Google Scholar
  7. 7.
    Völkel, M., Krötzsch, M., Vrandecic, D., Haller, H., Studer, R.: Semantic Wikipedia. In: WWW 2006 (2006)Google Scholar
  8. 8.
    Kozlova, N.: Automatic Ontology Extraction for document classification. In: Weikum, G. (ed.) A thesis submitted in conformity with the requirements for the degree of Master of Science (2005)Google Scholar
  9. 9.
    Kinzler, D.: WikiSense: Mining the Wiki. Wikimania (2005)Google Scholar
  10. 10.
    R. Bunescu. Using Encyclopedic Knowledge for Named Entity Disambiguation. EACL 2006 (2006) Google Scholar
  11. 11.
    Giles, J.: Internet encyclopaedias go head to head. Nature 438(7070), 900–901 (2005)CrossRefGoogle Scholar
  12. 12.
    Swartz, A.: Raw Thought: Who Writes Wikipedia? (2006), http://www.aaronsw.com/weblog/whowriteswikipedia
  13. 13.
    Swartz, A.: Raw Thought: Making More Wikipedians (2006), http://www.aaronsw.com/weblog/morewikipedians
  14. 14.
    Denoyer, L., Gallinari, P.: The Wikipedia XML Corpus. ACM SIGIR Forum 40(1) (June 2006)Google Scholar
  15. 15.
    Zhang, Y.: Wiki Means More: Hyperreading in Wikipedia. In: HT 2006 (2006)Google Scholar
  16. 16.
    Harth, A., Gassert, H., O’Murchu, I., Breslin, J., Decker, S.: Wikiont: An ontology for describing and exchanging wikipedia articles. Wikimania 2005  (2005)Google Scholar
  17. 17.
    Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity Flooding: A Versatile Graph Matching Algorithm and its Application to Schema Matching. In: ICDE 2002 (2002)Google Scholar
  18. 18.
    Gold, S., Rangarajan, A.: A graduated assignment algorithm for graph matching. In: TPAMI 1996 (1996)Google Scholar
  19. 19.
    Oldakowski, R., Bizer, C.: SemMF: A Framework for Calculating Semantic Similarity of Objects Represented as RDF Graphs. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, Springer, Heidelberg (2005)Google Scholar
  20. 20.
    Zhong, J., Zhu, H., Li, J., Yu, Y.: Conceptual Graph Matching for Semantic Search. In: Priss, U., Corbett, D.R., Angelova, G. (eds.) ICCS 2002. LNCS (LNAI), vol. 2393, Springer, Heidelberg (2002)CrossRefGoogle Scholar
  21. 21.
    Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: KDD 2002 (2002)Google Scholar
  22. 22.
    Zlatic, V., Bozicevic, M., Stefancic, H., Domazet, M.: Wikipedias: Collaborative web-based encyclopedias as complex networks. In: arXiv 2006 (2006), http://arxiv.org/pdf/physics/0602149
  23. 23.
    Gabrilovich, E., Markovitch, S.: Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge. In: AAAI 2006 (2006)Google Scholar
  24. 24.
    Salton, G., Lesk, M.E.: Computer evaluation of indexing and text processing. Journal of the ACM 15(1), 8–36Google Scholar
  25. 25.
    Salton, G.: The SMART Retrieval System - Experiments in Automatic Document Processing. Prentice-Hall, Englewood Cliffs (1971)Google Scholar
  26. 26.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, Boston, MA (1997)zbMATHGoogle Scholar
  27. 27.
    Liu, T-Y., Yang, Y., Wan, H., Zeng, H-J., Chen, Z., Ma, W-Y.: Support Vector Machines Classification with A Very Large-scale Taxonomy. SIGKDD Explorations 7(1), 36–43Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Linyun Fu
    • 1
  • Haofen Wang
    • 1
  • Haiping Zhu
    • 1
  • Huajie Zhang
    • 1
  • Yang Wang
    • 1
  • Yong Yu
    • 1
  1. 1.Apex Data and Knowledge Management Lab, Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240P.R. China

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