Dynamic Discovery of Type Classes and Relations in Semantic Web Data
This addresses the challenge of processing large semantic graph data on the Semantic Web, but it appears incremental as it builds on existing summarization techniques.
The authors tackled the problem of graph summarization in RDF graphs to reduce computational complexity, proposing an approach that automatically builds summary graph structures and discovers type classes with an optimum dissimilarity threshold.
The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have 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. 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.