Abstract
Different people or objects may share identical names in the real world, which causes confusion in many applications. It is a nontrivial task to distinguish those objects, especially when there is only very limited information associated with each of them. In this paper, we develop a general object distinction methodology called DISTINCT, which combines two complementary measures for relational similarity: set resemblance of neighbor tuples and random walk probability, and uses SVM to weigh different types of linkages without manually labeled training data. Experiments show that DISTINCT can accurately distinguish different objects with identical names in real databases.
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