AI Magazine
Ontology summarization based on rdf sentence graph
Proceedings of the 16th international conference on World Wide Web
Ontology visualization methods—a survey
ACM Computing Surveys (CSUR)
Toward a New Generation of Semantic Web Applications
IEEE Intelligent Systems
Winnowing ontologies based on application use
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Concept and Role Forgetting in ${\mathcal {ALC}}$ Ontologies
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Evaluations of user-driven ontology summarization
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
Extracting relevant questions to an RDF dataset using formal concept analysis
Proceedings of the sixth international conference on Knowledge capture
A novel approach to visualizing and navigating ontologies
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
BipRank: ranking and summarizing RDF vocabulary descriptions
JIST'11 Proceedings of the 2011 joint international conference on The Semantic Web
Enhancing LOD Complex Query Building with Context
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Attract me!: how could end-users identify interesting resources?
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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In this paper we address the issue of identifying the concepts in an ontology, which best summarize what the ontology is about. Our approach combines a number of criteria, drawn from cognitive science, network topology, and lexical statistics. In the paper we show two versions of our algorithm, which have been evaluated against the results produced by human experts. We report that the latest version of the algorithm performs very well, exhibiting an excellent degree of correlation with the choices of the experts. While the generation of automatic methods for ontology summarization is an interesting research issue in itself, the work described here also provides a basis for novel approaches to a variety of ontology engineering tasks, including ontology matching, automatic classification, ontology modularization, and ontology evaluation.