Dynamic Discovery of Type Classes and Relations in Semantic Web Data

Abstract

With the rapidly growing resource description framework (RDF) data on the Semantic Web, processing large semantic graph data has become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data based on instance similarities. To scale our approach, we utilized locality-sensitive hashing technique for identifying instance pairs which are candidates to be in the same type class. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method. © 2019 Elsevier B.V., All rights reserved.