TripleRank: Ranking Semantic Web Data by Tensor Decomposition

Abstract. The Semantic Web fosters novel applications targeting a more efficient and satisfying exploitation of the data available on the web, e.g. faceted browsing of linked open data. Large amounts and high diversity of knowledge in the Semantic Web pose the challenging question of appropriate relevance ranking for producing fine-grained and rich descriptions of the available data, e.g. to guide the user along most promising knowledge aspects. Existing methods for graphbased authority ranking lack support for fine-grained latent coherence between resources and predicates (i.e. support for link semantics in the linked data model). In this paper, we present TripleRank, a novel approach for faceted authority ranking in the context of RDF knowledge bases. TripleRank captures the additional latent semantics of Semantic Web data by means of statistical methods in order to produce richer descriptions of the available data. We model the Semantic Web by a 3-dimensional tensor that enables ...

• Type: Conference
• Year: 2009
• Where: SEMWEB
• Authors: Thomas Franz, Antje Schultz, Sergej Sizov, Steffen Staab
• Keywords: Authority Ranking, Faceted Browsing, Internet Technology, Semantic Web, SEMWEB 2009