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Entity Resolution in Texts Using Statistical Learning and Ontologies

  • Tadej Štajner
  • Dunja Mladenić
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5926)

Abstract

Ambiguities, which are inherently present in natural languages represent a challenge of determining the actual identities of entities mentioned in a document (e.g., Paris can refer to a city in France but it can also refer to a small city in Texas, USA or to a 1984 film directed by Wim Wenders having title Paris, Texas). Disambiguation is a problem that can be successfully solved by entity resolution methods.

This paper studies various methods for estimating relatedness between entities, used in collective entity resolution. We define a unified entity resolution approach, capable of using implicit as well as explicit relatedness for collectively identifying in-text entities. As a relatedness measure, we propose a method, which expresses relatedness using the heterogeneous relations of a domain ontology. We also experiment with other relatedness measures, such as using statistical learning of co-occurrences of two entities or using content similarity between them. Evaluation on real data shows that the new methods for relatedness estimation give good results.

Keywords

Entity resolution text mining semantic annotation ontology mapping 

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References

  1. 1.
    Mladenić, D.: Text Mining: Machine Learning on Documents. In: Encyclopedia of Data Warehousing and Mining, pp. 1109–1112 (2006)Google Scholar
  2. 2.
    Fellegi, I., Sunter, A.: A theory for record linkage. Journal of the American Statistical Association, 1183–1210 (1969)Google Scholar
  3. 3.
    Haas, L., Miller, R., Niswonger, B., Roth, M., Schwarz, P., Wimmers, E.: Transforming heterogeneous data with database middleware: Beyond integration. IEEE Data Engineering Bulletin 22(1), 31–36 (1999)Google Scholar
  4. 4.
    Winkler, W.: The state of record linkage and current research problems. Statistical Research Division, US Bureau of the Census, Wachington, DC (1999)Google Scholar
  5. 5.
    Tejada, S., Knoblock, C., Minton, S.: Learning object identification rules for information integration. Information Systems 26(8), 607–633 (2001)MATHCrossRefGoogle Scholar
  6. 6.
    Elmagarmid, A., Ipeirotis, P., Verykios, V.: Duplicate Record Detection: A Survey (2006)Google Scholar
  7. 7.
    Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of the 33rd annual meeting on Association for Computational Linguistics, pp. 189–196. Association for Computational Linguistics, Morristown (1995)CrossRefGoogle Scholar
  8. 8.
    Kalashnikov, D., Mehrotra, S.: A probabilistic model for entity disambiguation using relationships. In: SIAM International Conference on Data Mining (SDM), Newport Beach, California, pp. 21–23 (2005)Google Scholar
  9. 9.
    Bhattacharya, I., Getoor, L.: Collective entity resolution in relational data (2007)Google Scholar
  10. 10.
    Schütze, H.: Automatic word sense discrimination. Computational Linguistics 24(1), 97–123 (1998)Google Scholar
  11. 11.
    Bunescu, R., Pasca, M.: Using encyclopedic knowledge for named entity disambiguation. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, pp. 3–7 (2006)Google Scholar
  12. 12.
    Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 708–716 (2007)Google Scholar
  13. 13.
    Klyne, G., Carroll, J., McBride, B.: Resource description framework (RDF): Concepts and abstract syntax. W3C recommendation 10 (2004)Google Scholar
  14. 14.
    Bizer, C., Seaborne, A.: D2RQ-treating non-RDF databases as virtual RDF graphs. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298. Springer, Heidelberg (2004)Google Scholar
  15. 15.
    McCallum, A.: Information extraction: Distilling structured data from unstructured text. Queue 3(9), 48–57 (2005)CrossRefGoogle Scholar
  16. 16.
    Lloyd, L., Bhagwan, V., Gruhl, D., Tomkins, A.: Disambiguation of references to individuals. IBM Research Report (2005)Google Scholar
  17. 17.
    Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)MATHCrossRefGoogle Scholar
  18. 18.
    Mihalcea, R.: Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labeling. In: Proceedings of the conference on Human Language Technology and EMNLP, pp. 411–418. Association for Computational Linguistics, Morristown (2005)Google Scholar
  19. 19.
    Singla, P., Domingos, P.: Entity resolution with markov logic. In: Proceedings of the Sixth IEEE International Conference on Data Mining, pp. 572–582 (2006)Google Scholar
  20. 20.
    Chen, Z., Kalashnikov, D., Mehrotra, S.: Adaptive graphical approach to entity resolution. In: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, pp. 204–213. ACM, New York (2007)Google Scholar
  21. 21.
    Ramakrishnan, C., Milnor, W.H., Perry, M., Sheth, A.P.: Discovering informative connection subgraphs in multi-relational graphs. SIGKDD Explor. Newsl. 7(2), 56–63 (2005)CrossRefGoogle Scholar
  22. 22.
    Štajner, T.: From unstructured to linked data: entity extraction and disambiguation by collective similarity maximization, Identity and reference in web-base knowledge representation workshop (2009)Google Scholar
  23. 23.
    Li, X., Morie, P., Roth, D.: Semantic integration in text: From ambiguous names to identifiable entities. AI Magazine. Special Issue on Semantic Integration 26(1), 45–58 (2005)Google Scholar
  24. 24.
    Bunescu, R., Mooney, R., Ramani, A., Marcotte, E.: Integrating co-occurrence statistics with information extraction for robust retrieval of protein interactions from Medline. In: Proceedings of the BioNLP Workshop on Linking NLP Processing and Biology at HLTNAACL, vol. 6, pp. 49–56 (2006)Google Scholar
  25. 25.
    Overell, S., Magalhaes, J., Ruger, S.: Place disambiguation with co-occurrence models. In: CLEF 2006 Workshop, Working notes (2006)Google Scholar
  26. 26.
    Yates, A., Etzioni, O.: Unsupervised resolution of objects and relations on the Web. In: Proceedings of NAACL HLT, pp. 121–130 (2007)Google Scholar
  27. 27.
    Finkel, J., Grenager, T., Manning, C.: Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Ann Arbor 100 (2005)Google Scholar
  28. 28.
    Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string distance metrics for name-matching tasks. In: Proceedings of the IJCAI-2003 Workshop on Information Integration on the Web, IIWeb 2003 (2003)Google Scholar
  29. 29.
    Jang, M., Myaeng, S., Park, S.: Using mutual information to resolve query translation ambiguities and query term weighting. In: Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, pp. 223–229. Association for Computational Linguistics, Morristown (1999)CrossRefGoogle Scholar
  30. 30.
    Church, K., Hanks, P.: Word association norms, mutual information, and lexicography. Computational Linguistics 16(1), 22–29 (1990)Google Scholar
  31. 31.
    Li, H., Abe, N.: Word clustering and disambiguation based on cooccurrence data. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, vol. 2, pp. 749–755. Association for Computational Linguistics, Morristown (1998)CrossRefGoogle Scholar
  32. 32.
    Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)MATHGoogle Scholar
  33. 33.
    Sandhaus, E: The New York Times Annotated Corpus, 2008.40 Google Scholar
  34. 34.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: Dbpedia: A nucleus for a web of open data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., 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
  35. 35.
    Suchanek, F., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th international conference on World Wide Web, pp. 697–706. ACM, New York (2007)CrossRefGoogle Scholar
  36. 36.
    Suchanek, F.M., Sozio, M., Weikum, G.: Sofie: a self-organizing framework for information extraction. In: WWW 2009: Proceedings of the 18th international conference on World wide web, pp. 631–640. ACM, New York (2009)CrossRefGoogle Scholar
  37. 37.
    Decker, S., Melnik, S., Van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., Erdmann, M., Horrocks, I.: The semantic web: The roles of XML and RDF. IEEE Internet Computing 4(5), 63–73 (2000)CrossRefGoogle Scholar
  38. 38.
    Fortuna, B., Grobelnik, M., Mladenić, D.: Visualization of text document corpus. Special Issue: Hot Topics in European Agent Research 29, 497–502 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tadej Štajner
    • 1
  • Dunja Mladenić
    • 1
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia

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