Mining Equivalent Relations from Linked Data
ziqi zhang, Anna Lisa Gentile, Isabelle Augenstein, Eva Blomqvist and Fabio Ciravegna
The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013
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.
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