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Merging and ranking answers in the Semantic Web: the wisdom of crowds

Lopez, Vanessa; Nikolov, Andriy; Fernandez, Miriam; Sabou, Marta; Uren, Victoria and Motta, Enrico (2009). Merging and ranking answers in the Semantic Web: the wisdom of crowds. In: The Semantic Web.

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In this paper we propose algorithms for combining and ranking answers from distributed heterogeneous data sources in the context of a multi-ontology Question Answering task. Our proposal includes a merging algorithm that aggregates, combines and filters ontology-based search results and three different ranking algorithms that sort the final answers according to different criteria such as popularity, confidence and semantic interpretation of results. An experimental evaluation on a large scale corpus indicates improvements in the quality of the search results with respect to a scenario where the merging and ranking algorithms were not applied. These collective methods for merging and ranking allow to answer questions that are distributed across ontologies, while at the same time, they can filter irrelevant answers, fuse similar answers together, and elicit the most accurate answer(s) to a question.

Item Type: Conference or Workshop Item
Copyright Holders: 2009 Springer-Verlag Berlin Heidelberg
ISSN: 0302-9743
Keywords: merging; ranking; fusion; question answering; Semantic Web
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 23445
Depositing User: Kay Dave
Date Deposited: 14 Oct 2010 11:39
Last Modified: 04 Jul 2020 17:35
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