Publication details for Professor Alexandra CristeaZhou, Yiwei, Demidova, Elena & Cristea, A. I. (2017). What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval. In Transactions on Computational Collective Intelligence XXVI. Nguyen, N., Kowalczyk, R., Pinto, A. & Cardoso, J. Cham: Springer. 10190: 2010-231.
- Publication type: Chapter in book
- ISSN/ISBN: 9783319592671, 9783319592688
- DOI: 10.1007/978-3-319-59268-8_10
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
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Author(s) from Durham
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.