Interleaving clustering of classes and properties for disambiguating linked data

Abstract

As Linked Data (or LD) increasingly expands its capacity, ambiguity in vocabularies on LD has become more problematic. This paper deals with a part of the ambiguity, namely, class ambiguity and property ambiguity. In this paper, we propose a novel clustering method, CPClustering, which clusters synonymous classes and properties in an interleaving manner. CPClustering groups classes by their related properties, and, inversely, groups properties by their related classes. CPClustering iteratively clusters classes and properties, and updates their representations in terms of immediate clustering results.