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What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval
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Zhou, Yiwei, Demidova, Elena and Cristea, Alexandra I. (2017) What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval. In: Nguyen , N. and Kowalczyk, R. and Pinto, A. and Cardoso , J., (eds.) Transactions on Computational Collective Intelligence XXVI. Lecture Notes in Computer Science, 10190 . Cham: Springer, pp. 2010-231. ISBN 9783319592671
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Official URL: https://doi.org/10.1007/978-3-319-59268-8_10
Abstract
Representation of influential entities, such as celebrities and multinational corporations on the web can vary across languages, re- flecting language-specific entity aspects, as well as divergent views on these entities in different communities. An important source of multilingual background knowledge about influential entities is Wikipedia — an online community-created encyclopaedia — containing more than 280 language editions. Such language-specific information could be applied in entity-centric information retrieval applications, in which users utilise very simple queries, mostly just the entity names, for the relevant documents. In this article we focus on the problem of creating languagespecific entity contexts to support entity-centric, language-specific information retrieval applications. First, we discuss alternative ways such contexts can be built, including Graph-based and Article-based approaches. Second, we analyse the similarities and the differences in these contexts in a case study including 220 entities and five Wikipedia language editions. Third, we propose a context-based entity-centric information retrieval model that maps documents to aspect space, and apply languagespecific entity contexts to perform query expansion. Last, we perform a case study to demonstrate the impact of this model in a news retrieval application. Our study illustrates that the proposed model can effectively improve the recall of entity-centric information retrieval while keeping high precision, and provide language-specific results.
| Item Type: | Book Item | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | H Social Sciences > HM Sociology Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Celebrities, Information retrieval, News Web sites, International business enterprises | ||||||
| Series Name: | Lecture Notes in Computer Science | ||||||
| Journal or Publication Title: | Transactions on Computational Collective Intelligence | ||||||
| Publisher: | Springer | ||||||
| Place of Publication: | Cham | ||||||
| ISBN: | 9783319592671 | ||||||
| ISSN: | 2190-9288 | ||||||
| Book Title: | Transactions on Computational Collective Intelligence XXVI | ||||||
| Editor: | Nguyen , N. and Kowalczyk, R. and Pinto, A. and Cardoso , J. | ||||||
| Official Date: | 15 June 2017 | ||||||
| Dates: |
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| Volume: | 10190 | ||||||
| Page Range: | pp. 2010-231 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Date of first compliant deposit: | 14 February 2017 | ||||||
| Date of first compliant Open Access: | 27 July 2017 | ||||||
| Funder: | European Cooperation in the Field of Scientific and Technical Research (Organization) (COST), European Research Council (ERC), Horizon 2020 (European Commission) (H2020) | ||||||
| Grant number: | IC1302 KEYSTONE (COST), ALEXANDRIA ERC 339233 (ERC), H2020-MSCA-ITN-2014 WDAqua 64279 (H2020) | ||||||
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