Advertisement

Analysing Entity Context in Multilingual Wikipedia to Support Entity-Centric Retrieval Applications

  • Yiwei Zhou
  • Elena Demidova
  • Alexandra I. Cristea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9398)

Abstract

Representation of influential entities, such as famous people and multinational corporations, on the Web can vary across languages, reflecting language-specific entity aspects as well as divergent views on these entities in different communities. A systematic analysis of language-specific entity contexts can provide a better overview of the existing aspects and support entity-centric retrieval applications over multilingual Web data. An important source of cross-lingual information about influential entities is Wikipedia — an online community-created encyclopaedia — containing more than 280 language editions. In this paper we focus on the extraction and analysis of the language-specific entity contexts from different Wikipedia language editions over multilingual data. We discuss alternative ways such contexts can be built, including graph-based and article-based contexts. Furthermore, we analyse the similarities and the differences in these contexts in a case study including 80 entities and five Wikipedia language editions.

Notes

Acknowledgments

This work was partially funded by the COST Action IC1302 (KEYSTONE) and the European Research Council under ALEXANDRIA (ERC 339233).

References

  1. 1.
    Bunescu, R.C., Pasca, M.: Using encyclopedic knowledge for named entity disambiguation. In: EACL, vol. 6, pp. 9–16Google Scholar
  2. 2.
    Cucerzan, S.: Large-scale named entity disambiguation based on wikipedia data. In: EMNLP-CoNLL, vol. 7, pp. 708–716Google Scholar
  3. 3.
    Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems, I-SEMANTICS 2013, pp. 121–124. ACM, New York (2013)Google Scholar
  4. 4.
    Egozi, O., Markovitch, S., Gabrilovich, E.: Concept-based information retrieval using explicit semantic analysis. ACM Trans. Inf. Syst. (TOIS) 29(2), 8 (2011)CrossRefGoogle Scholar
  5. 5.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: IJCAI, vol. 7, pp. 1606–1611Google Scholar
  6. 6.
    Gabrilovich, E., Markovitch, S.: Wikipedia-based semantic interpretation for natural language processing. J. Artif. Intell. Res. 34(1), 443–498 (2009)zbMATHGoogle Scholar
  7. 7.
    Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: a graph-based method. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 765–774. ACMGoogle Scholar
  8. 8.
    Han, X., Zhao, J.: Named entity disambiguation by leveraging wikipedia semantic knowledge. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 215–224. ACMGoogle Scholar
  9. 9.
    Hu, J., Fang, L., Cao, Y., Zeng, H.-J., Li, H., Yang, Q., Chen, Z.: Enhancing text clustering by leveraging wikipedia semantics. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 179–186. ACMGoogle Scholar
  10. 10.
    Kaptein, R., Kamps, J.: Exploiting the category structure of wikipedia for entity ranking. Artif. Intell. 194, 111–129 (2013)CrossRefzbMATHGoogle Scholar
  11. 11.
    Kataria, S.S., Kumar, K.S., Rastogi, R.R., Sen, P., Sengamedu, S.H.: Entity disambiguation with hierarchical topic models. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1037–1045. ACMGoogle Scholar
  12. 12.
    Kulkarni, S., Singh, A., Ramakrishnan, G., Chakrabarti, S.: Collective annotation of wikipedia entities in web text. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 457–466. ACMGoogle Scholar
  13. 13.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: Dbpedia spotlight: shedding light on the web of documents. In: Proceedings the 7th International Conference on Semantic Systems, I-SEMANTICS 2011, Graz, Austria, 7–9 September 2011, pp. 1–8 (2011)Google Scholar
  14. 14.
    Milne, D.N., Witten, I.H., Nichols, D.M.: A knowledge-based search engine powered by wikipedia. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 445–454. ACMGoogle Scholar
  15. 15.
    Nastase, V., Strube, M.: Transforming wikipedia into a large scale multilingual concept network. Artif. Intell. 194, 62–85 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Nothman, J., Ringland, N., Radford, W., Murphy, T., Curran, J.R.: Learning multilingual named entity recognition from wikipedia. Artif. Intell. 194, 151–175 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Otegi, A., Arregi, X., Ansa, O., Agirre, E.: Using knowledge-based relatedness for information retrieval. Knowl. Inf. Syst. 44(3), 1–30 (2014)Google Scholar
  18. 18.
    Ploch, D.: Exploring entity relations for named entity disambiguation. In: Proceedings of the ACL 2011 Student Session, pp. 18–23. Association for Computational LinguisticsGoogle Scholar
  19. 19.
    Rogers, R.: Wikipedia as Cultural Reference. In: Digital Methods. The MIT Press, Cambridge (2013)Google Scholar
  20. 20.
    Wang, P., Hu, J., Zeng, H.-J., Chen, Z.: Using wikipedia knowledge to improve text classification. Knowl. Inf. Syst. 19(3), 265–281 (2009)CrossRefGoogle Scholar
  21. 21.
    Witten, I., Milne, D.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In: Proceeding of AAAI Workshop on Wikipedia and Artificial Intelligence: an evolving synergy, pp. 25–30. AAAI Press, Chicago, USAGoogle Scholar
  22. 22.
    Yazdani, M., Popescu-Belis, A.: Computing text semantic relatedness using the contents and links of a hypertext encyclopedia. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 3185–3189. AAAI Press (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Yiwei Zhou
    • 1
  • Elena Demidova
    • 2
  • Alexandra I. Cristea
    • 1
  1. 1.Department of Computer ScienceUniversity of WarwickCoventryUK
  2. 2.L3S Research Center and Leibniz Universität HannoverHannoverGermany

Personalised recommendations