Finding and ranking knowledge on the semantic web

  • Authors:

  • Li Ding;Rong Pan;Tim Finin;Anupam Joshi;Yun Peng;Pranam Kolari

  • Affiliations:

  • Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD

  • Venue:
  • ISWC'05 Proceedings of the 4th international conference on The Semantic Web
  • Year:
  • 2005

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Abstract

Swoogle helps software agents and knowledge engineers find Semantic Web knowledge encoded in RDF and OWL documents on the Web. Navigating such a Semantic Web on the Web is difficult due to the paucity of explicit hyperlinks beyond the namespaces in URIrefs and the few inter-document links like rdfs:seeAlso and owl:imports. In order to solve this issue, this paper proposes a novel Semantic Web navigation model providing additional navigation paths through Swoogle's search services such as the Ontology Dictionary. Using this model, we have developed algorithms for ranking the importance of Semantic Web objects at three levels of granularity: documents, terms and RDF graphs. Experiments show that Swoogle outperforms conventional web search engine and other ontology libraries in finding more ontologies, ranking their importance, and thus promoting the use and emergence of consensus ontologies.