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A Link-Based Ranking Algorithm for Semantic Web Resources: A Class-Oriented Approach Independent of Link Direction
Abstract
The information space of the Semantic Web has different characteristics from that of the World Wide Web (WWW). One main difference is that in the Semantic Web, the direction of Resource Description Framework (RDF) links does not have the same meaning as the direction of hyperlinks in the WWW, because the link direction is determined not by a voting process but by a specific schema in the Semantic Web. Considering this fundamental difference, the authors propose a method for ranking Semantic Web resources independent of link directions and show the convergence of the algorithm and experimental results. This method focuses on the classes rather than the properties. The property weights are assigned depending on the relative significance of the property to the resource importance of each class. It solves some problems reported in prior studies, including the Tightly Knit Community (TKC) effect, as well as having higher accuracy and validity compared to existing methods.
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