Advertisement

Linking Entities in Unstructured Texts with RDF Knowledge Bases

  • Fang Du
  • Yueguo Chen
  • Xiaoyong Du
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)

Abstract

Entity linking (entity annotation) is the task of linking named entity mentions on Web pages with the entities of a knowledge base (KB). With the continued progress of information extraction and semantic search techniques, entity linking has received much attention in both research and industrial communities. The challenge of the task is mainly on entity disambiguation. To our best knowledge, the huge existing RDF KBs have not been fully exploited for entity linking. In this paper, we study the entity linking problem via the usage of RDF KBs. Besides the accuracy of entity linking, the scalability of handling huge Web corpus and large RDF KBs are also studied. The experimental results show that our solution on entity linking achieves not only very good accuracy but also good scalability.

Keywords

RDF knowledge base entity linking named entity disambiguation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Guha, R.V., McCool, R., Miller, E.: Semantic search. In: WWW, pp. 700–709 (2003)Google Scholar
  2. 2.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: A core of semantic knowledge unifying wordnet and wikipedia. In: WWW, pp. 697–706 (2007)Google Scholar
  3. 3.
    Bollacker, K.D., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250 (2008)Google Scholar
  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.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: OSDI 2004 (2004)Google Scholar
  7. 7.
    Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In: SIGDOC, Toronto (June 1986)Google Scholar
  8. 8.
    Mihalcea, R.: Large vocabulary unsupervised word sense disambiguation with graph-based algorithms for sequence data labeling. In: Proceedings of the Human Language Technology/Empirical Methods in Natural Language Processing Conference, Vancouver (2005)Google Scholar
  9. 9.
    Navigli, R., Velardi, P.: Structural semantic interconnections: a knowledge-based approach to word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 27 (2005)Google Scholar
  10. 10.
    Bunescu, R., Pasca, M.: Using Encyclopedic Knowledge for Named Entity Disambiguation. In: EACL, pp. 9–16 (2006)Google Scholar
  11. 11.
    Bagga, A., Baldwin, B.: Entity-based cross-document coreferencing using the vector space model. In: COLING, pp. 79–85 (1998)Google Scholar
  12. 12.
    Dredze, M., McNamee, P., Rao, D., Gerber, A., Finin, T.: Entity disambiguation for knowledge base population. In: COLING, pp. 277–285 (2010)Google Scholar
  13. 13.
    Hasegawa, T., Sekine, S., Grishman, R.: Discovering relations among named entities from large corpora. In: ACL, pp. 415–422 (2004)Google Scholar
  14. 14.
    Demartini, G., Difallah, D.E., Cudré-Mauroux, P.: ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In: WWW, pp. 469–478 (2012)Google Scholar
  15. 15.
    Stoyanov, V., Mayfield, J., Xu, T., Oard, D.W., Lawrie, D., Oates, T., Finnin, T.: A context aware approach to entity linking. In: The Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, NAACL-HLT 2012 (2012)Google Scholar
  16. 16.
    Navigli, R., Velardi, P.: Structural semantic interconnections: a knowledge-based approach to word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1075–1086 (2005)CrossRefGoogle Scholar
  17. 17.
    Gliozzo, A., Giuliano, C., Strapparava, C.: Domain kernels for word sense disambiguation. In: ACL (2005)Google Scholar
  18. 18.
    Ng, H., Lee, H.: Integrating multiple knowledge sources to disambiguate word sense: An examplar-based approach. CoRR, vol. 9606032 (1996)Google Scholar
  19. 19.
    Weikum, G., Theobald, M.: From information to knowledge: harvesting entities and relationships from web sources. In: PODS, pp. 65–76 (2010)Google Scholar
  20. 20.
    Hoffart, J., Yosef, M.A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., Weikum, G.: Robust Disambiguation of Named Entities in Text. In: Proceedings of EMNLP, pp. 782–792 (2011)Google Scholar
  21. 21.
    Shen, W., Wang, J.Y., Luo, P., Wang, M.: LINDEN: linking named entities with knowledge base via semantic knowledge. In: WWW 2012, pp. 449–458 (2012)Google Scholar
  22. 22.
    Shen, W., Wang, J.Y., Luo, P., Wang, M.: LIEGE: Link Entities in Web Lists with Knowledge Base. In: Proceedins of KDD 2012 (2012)Google Scholar
  23. 23.
    Carlos, B.T., Guestrin, C., Koller, D.: Max-margin markov networks. In: NIPS (2003)Google Scholar
  24. 24.
    Nadeau, D., Turney, P.D., Matwin, S.: Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity. In: Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006. LNCS (LNAI), vol. 4013, pp. 266–277. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
  26. 26.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fang Du
    • 1
    • 2
  • Yueguo Chen
    • 1
  • Xiaoyong Du
    • 1
  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.School of Mathematics and Computer ScienceNingxia UniversityChina

Personalised recommendations