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.
In REF , authors address the problem of mining equivalent relations from Linked Data datasets as a clustering problem using an equivalence score.
Mining Equivalent Relations from Linked Data
ACL
Computer Science