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

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Book cover Semantic Keyword-Based Search on Structured Data Sources (IKC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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Notes

  1. 1.

    http://en.wikipedia.org/wiki/List_of_Wikipedias.

References

  1. Bunescu, R.C., Pasca, M.: Using encyclopedic knowledge for named entity disambiguation. In: EACL, vol. 6, pp. 9–16

    Google Scholar 

  2. Cucerzan, S.: Large-scale named entity disambiguation based on wikipedia data. In: EMNLP-CoNLL, vol. 7, pp. 708–716

    Google Scholar 

  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. Egozi, O., Markovitch, S., Gabrilovich, E.: Concept-based information retrieval using explicit semantic analysis. ACM Trans. Inf. Syst. (TOIS) 29(2), 8 (2011)

    Article  Google Scholar 

  5. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: IJCAI, vol. 7, pp. 1606–1611

    Google Scholar 

  6. Gabrilovich, E., Markovitch, S.: Wikipedia-based semantic interpretation for natural language processing. J. Artif. Intell. Res. 34(1), 443–498 (2009)

    Article  Google Scholar 

  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. ACM

    Google Scholar 

  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. ACM

    Google Scholar 

  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. ACM

    Google Scholar 

  10. Kaptein, R., Kamps, J.: Exploiting the category structure of wikipedia for entity ranking. Artif. Intell. 194, 111–129 (2013)

    Article  Google Scholar 

  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. ACM

    Google Scholar 

  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. ACM

    Google Scholar 

  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. 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. ACM

    Google Scholar 

  15. Nastase, V., Strube, M.: Transforming wikipedia into a large scale multilingual concept network. Artif. Intell. 194, 62–85 (2013)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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. Ploch, D.: Exploring entity relations for named entity disambiguation. In: Proceedings of the ACL 2011 Student Session, pp. 18–23. Association for Computational Linguistics

    Google Scholar 

  19. Rogers, R.: Wikipedia as Cultural Reference. In: Digital Methods. The MIT Press, Cambridge (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  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, USA

    Google Scholar 

  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 

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Computer Science, University of Warwick, Coventry, UK

    Yiwei Zhou & Alexandra I. Cristea

  2. L3S Research Center and Leibniz Universität Hannover, Hannover, Germany

    Elena Demidova

Authors
  1. Yiwei Zhou
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  2. Elena Demidova
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  3. Alexandra I. Cristea
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Corresponding author

Correspondence to Yiwei Zhou .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Jorge Cardoso

  2. Huawei European Research Center, Munich, Germany

    Jorge Cardoso

  3. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Francesco Guerra

  4. Delft University of Technology, Delft, Zuid-Holland, The Netherlands

    Geert-Jan Houben

  5. University of Coimbra, Coimbra, Portugal

    Alexandre Miguel Pinto

  6. Università degli Studi di Trento, Trento, Italy

    Yannis Velegrakis

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Zhou, Y., Demidova, E., Cristea, A.I. (2015). Analysing Entity Context in Multilingual Wikipedia to Support Entity-Centric Retrieval Applications. In: Cardoso, J., Guerra, F., Houben, GJ., Pinto, A.M., Velegrakis, Y. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2015. Lecture Notes in Computer Science(), vol 9398. Springer, Cham. https://doi.org/10.1007/978-3-319-27932-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-27932-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27931-2

  • Online ISBN: 978-3-319-27932-9

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