Terminology Learning through Taxonomy Discovery

Abstract

Description Logics based languages have emerged as the standard knowledge representation scheme for ontologies. Typically, an ontology formalizes a number of dependent and related concepts in a domain, encompassed as a terminology. As defining such terminologies manually is a complex, time consuming and error-prone task, there is great interest and even demands for methods that learn terminologies automatically. Learning a terminology in Descriptions Logics concerns to learn several related concepts. This process would greatly benefit of an ideal order to determine which concept should be learned before another concept. Arguably, such an order would yield rich and readable terminologies, as previously, and interrelated concepts formerly learned could be used to induce the description of further concepts. In this work, we contribute with a formal definition of the concept and terminology learning problems and from such definitions we devise an algorithm for finding an ordering through concept taxonomy discovery, that should be followed when learning several related concepts. We show through an experiment that by following the order detected by the algorithm, we are able to afford a more readable terminology than methods that do not conceive an ideal order or do not learn concepts in a dependent way.

DOI: 10.1109/BRACIS.2013.36

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Cite this paper

@article{Melo2013TerminologyLT, title={Terminology Learning through Taxonomy Discovery}, author={Raphael Melo and Kate Revoredo and Aline Paes}, journal={2013 Brazilian Conference on Intelligent Systems}, year={2013}, pages={169-174} }