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A Comprehensive Extraction of Relevant Real-World-Event Qualifiers for Semantic Search Engines

Abstract : In this paper, we present an efficient and accurate method to represent events from numerous public sources, such as Wikidata or more specific knowledge bases. We focus on events happening in the real world, such as festivals or assassinations. Our method merges knowledge from Wikidata and Wikipedia article summaries to gather entities involved in events, dates, types and labels. This event characterization procedure is extended by including vernacular languages. Our method is evaluated by a comparative experiment on two datasets that shows that events are represented more accurately and exhaustively with vernacular languages. This can help to extend the research that mainly exploits hub languages, or biggest language editions of Wikipedia. This method and the tool we release will for instance enhance event-centered semantic search engines, a context in which we already use it. An additional contribution of this paper is the public release of the source code of the tool, as well as the corresponding datasets.
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Contributor : Guillaume Bernard Connect in order to contact the contributor
Submitted on : Tuesday, August 31, 2021 - 12:21:47 PM
Last modification on : Thursday, May 12, 2022 - 3:34:51 PM
Long-term archiving on: : Wednesday, December 1, 2021 - 9:10:39 PM


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Guillaume Bernard, Cyrille Suire, Cyril Faucher, Antoine Doucet. A Comprehensive Extraction of Relevant Real-World-Event Qualifiers for Semantic Search Engines. Linking Theory and Practice of Digital Libraries, Sep 2021, Online, France. pp.153-164, ⟨10.1007/978-3-030-86324-1_19⟩. ⟨hal-03329842⟩



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