A Similarity-Oriented RDF Graph Matching Algorithm for Ranking Linked Data

Linked Data is an RDF-based transition from the document oriented Web into the Semantic Web, and the amount of data published as linked data steadily. Hence, The RDF graph matching algorithm becomes the technical foundation of many tasks in Semantic Web, such as semantic search, data fusion, ontology matching, data filter and dissemination. An RDF-graph-matching-based query can enables searching with additional semantic information, so that it can be utilized for obtaining expected ranking in semantic search and personalized information retrieval on the web of data. Yet, the need for this approach is disregarded. This paper proposes a novel similarity-oriented RDF graph matching approach for ranking linked data, which considering the element-level and structure-level similarity of statements, and also the similarity of URIs and blank nodes in RDF graphs. The efficiency of this approach is improved over the traditional RDF graph matching algorithm. And the effectiveness is improved by analyzing and measuring the structure-level similarity of statements. The experimental results shows that this approach can effectively measure the similarity between RDF graphs, and also returns results with respect to a query RDF graph as a ranked set of promising alternatives.