Abstract
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.
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Peroni, S., Motta, E., d’Aquin, M. (2008). Identifying Key Concepts in an Ontology, through the Integration of Cognitive Principles with Statistical and Topological Measures. In: Domingue, J., Anutariya, C. (eds) The Semantic Web. ASWC 2008. Lecture Notes in Computer Science, vol 5367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89704-0_17
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DOI: https://doi.org/10.1007/978-3-540-89704-0_17
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