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Mining Equivalent Relations from Linked Data
University of Sheffield, UK.
University of Sheffield, UK.
University of Sheffield, UK.
Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology. (MDA)ORCID iD: 0000-0003-0036-6662
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2013 (English)In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Volume 2: Short Papers, Association for Computational Linguistics, 2013, 289-293 p.Conference paper, Published paper (Refereed)
Abstract [en]

Linking heterogeneous resources is a major research challenge in the Semantic Web. This paper studies the task of mining equivalent relations from Linked Data, which was insufficiently addressed before. We introduce an unsupervised method to measure equivalency of relation pairs and cluster equivalent relations. Early experiments have shown encouraging results with an average of 0.75~0.87 precision in predicting relation pair equivalency and 0.78~0.98 precision in relation clustering.

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2013. 289-293 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-112231ISBN: 978-1-937284-51-0 (print)OAI: oai:DiVA.org:liu-112231DiVA: diva2:764421
Conference
51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), 4-9 August 2013, Sofia, Bulgaria
Available from: 2014-11-19 Created: 2014-11-19 Last updated: 2017-03-20

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Blomqvist, Eva

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Citation style
  • apa
  • harvard1
  • ieee
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  • Other locale
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Output format
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