Paper: Mining Equivalent Relations from Linked Data

ACL ID P13-2052
Title Mining Equivalent Relations from Linked Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013

Linking heterogeneous resources is a major re- search challenge in the Semantic Web. This paper studies the task of mining equivalent re- lations from Linked Data, which was insuffi- ciently addressed before. We introduce an un- supervised 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.