Do We Need Entity-Centric Knowledge Bases for Entity Disambiguation?

ABSTRACT

Entity Disambiguation has been studied extensively in the last 10 years with authors reporting increasingly well performing systems. However, most studies focused on general purpose knowledge bases like Wikipedia or DBPedia and left out the question how those results generalize to more specialized domains. This is especially important in the context of Linked Open Data which forms an enormous resource for disambiguation. However, the influence of domain heterogeneity, size and quality of the knowledge base remains largely unanswered. In this paper we present an extensive set of experiments on special purpose knowledge bases from the biomedical domain where we evaluate the disambiguation performance along four variables: (i) the representation of the knowledge base as being either entity-centric or document-centric, (ii) the size of the knowledge base in terms of entities covered, (iii) the semantic heterogeneity of a domain and (iv) the quality and completeness of a knowledge base. Our results show that for special purpose knowledge bases (i) document-centric disambiguation significantly outperforms entity-centric disambiguation, (ii) document-centric disambiguation does not depend on the size of the knowledge-base, while entity-centric approaches do, and (iii) disambiguation performance varies greatly across domains. These results suggest that domain-heterogeneity, size and knowledge base quality have to be carefully considered for the design of entity disambiguation systems and that for constructing knowledge bases user-annotated texts are preferable to carefully constructed knowledge bases.

References

1. R. Bunescu and M. Pasca. Using encyclopedic knowledge for named entity disambiguation. In Proceedings of EACL, volume 6, pages 9--16, 2006.
2. S. Cucerzan. 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), pages 708--716, Prague, Czech Republic, June 2007. Association for Computational Linguistics.
3. M. Dredze, P. McNamee, D. Rao, A. Gerber, and T. Finin. Entity disambiguation for knowledge base population. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 277--285. Association for Computational Linguistics, 2010.
4. A. L. Gentile, Z. Zhang, L. Xia, and J. Iria. Graph-based Semantic Relatedness for Named Entity Disambiguation. In 1st International Conference on Software, Services and Semantic Technologies, 2009.
5. H. Han, L. Giles, H. Zha, C. Li, and K. Tsioutsiouliklis. Two supervised learning approaches for name disambiguation in author citations. In Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries, JCDL '04, pages 296--305, New York, NY, USA, 2004. ACM.
6. X. Han and J. Zhao. Named entity disambiguation by leveraging wikipedia semantic knowledge. In Proceedings of the 18th ACM conference on Information and knowledge management, CIKM '09, pages 215--224, New York, NY, USA, 2009. ACM.
7. K. S. Jones, S. Walker, and S. E. Robertson. A probabilistic model of information retrieval: development and comparative experiments. Inf. Process. Manage., 36(6), 2000.
8. S. Kafkas, I. Lewin, D. Milward, E. van Mulligen, J. Kors, U. Hahn, and D. Rebholz-Schuhmann. Calbc: Releasing the final corpora. In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12), Istanbul, Turkey, May 2012.
9. R. Kern, M. Zechner, and M. Granitzer. Model selection strategies for author disambiguation. In Database and Expert Systems Applications (DEXA), 2011 22nd International Workshop on, pages 155--159. IEEE, 2011.
10. T.-Y. Liu. Learning to Rank for Information Retrieval. Springer, 2011.
11. A. Luberg, M. Granitzer, H. Wu, P. Järv, and T. Tammet. Information retrieval and deduplication for tourism recommender sightsplanner. In Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, page 50. ACM, 2012.
12. C. D. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA, 2008.
13. A. McCray, A. Burgun, and O. Bodenreider. Aggregating umls semantic types for reducing conceptual complexity. Proceedings of Medinfo, 10(pt 1):216--20, 2001.
14. P. N. Mendes, M. Jakob, A. García-Silva, and C. Bizer. Dbpedia spotlight: shedding light on the web of documents. In Proceedings of the 7th International Conference on Semantic Systems, I-Semantics '11, pages 1--8, New York, NY, USA, 2011. ACM.
15. R. Mihalcea and A. Csomai. Wikify!: linking documents to encyclopedic knowledge. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pages 233--242. ACM, 2007.
16. Y. Peng, D. He, and M. Mao. Geographic named entity disambiguation with automatic profile generation. In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, WI '06, pages 522--525, Washington, DC, USA, 2006. IEEE Computer Society.
17. M. F. Porter. An algorithm for suffix stripping. Program, 14(3):130--137, July 1980.
18. L. Ratinov, D. Roth, D. Downey, and M. Anderson. Local and global algorithms for disambiguation to wikipedia. In Proceedings of the Annual Meeting of the Association of Computational Linguistics, 2011.
19. G. Salton. Automatic Information Organization and Retrieval. McGraw Hill Text, 1968.
20. G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Communications of the ACM, 18(11):613--620, Nov. 1975.
21. J. Wang, G. Li, J. X. Yu, and J. Feng. Entity matching: How similar is similar. PVLDB, 4(10):622--633, 2011.