Finding and Ranking Knowledge on the Semantic Web
Lecture notes in computer sciencePublished 1 January 2005
Li Ding, Rong Pan, Tim Finin, Anupam Joshi, Yun Peng, Pranam Kolari
Citations237
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
A novel Semantic Web navigation model providing additional navigation paths through Swoogle's search services such as the Ontology Dictionary is proposed, and algorithms for ranking the importance ofSemantic Web objects at three levels of granularity: documents, terms and RDF graphs are developed.
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.
Keywords
Computer ScienceDecision Sciences
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