Mining Equivalent Relations from Linked Data
2013 (English)In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Volume 2: Short Papers, 2013, 289-293 p.Conference paper (Refereed)
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
2013. 289-293 p.
IdentifiersURN: urn:nbn:se:liu:diva-112231ISBN: 978-1-937284-51-0OAI: oai:DiVA.org:liu-112231DiVA: diva2:764421
51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), 4-9 August 2013, Sofia, Bulgaria