Identifying Potentially Important Concepts and Relations in an Ontology

  • Authors:

  • Gang Wu;Juanzi Li;Ling Feng;Kehong Wang

  • Affiliations:

  • Department of Computer Science, Tsinghua University, Beijing, P.R. China 100084 and Department of Computer Science, Southeastern University, Nanjing, P.R. China 210000;Department of Computer Science, Tsinghua University, Beijing, P.R. China 100084;Department of Computer Science, Tsinghua University, Beijing, P.R. China 100084;Department of Computer Science, Tsinghua University, Beijing, P.R. China 100084

  • Venue:
  • ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
  • Year:
  • 2008

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Abstract

More and more ontologies have been published and used widely on the web. In order to make good use of an ontology, especially a new and complex ontology, we need methods to help understand it first. Identifying potentially important concepts and relations in an ontology is an intuitive but challenging method. In this paper, we first define four features for potentially important concepts and relation from the ontological structural point of view. Then a simple yet effective Concept-And-Relation-Ranking (CARRank ) algorithm is proposed to simultaneously rank the importance of concepts and relations. Different from the traditional ranking methods, the importance of concepts and the weights of relations reinforce one another in CARRank in an iterative manner. Such an iterative process is proved to be convergent both in principle and by experiments. Our experimental results show that CARRank has a similar convergent speed as the PageRank-like algorithms, but a more reasonable ranking result.