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