Data-driven diachronic and categorical evaluation of ontologies: Framework, measure, and metrics

Hlomani Hlomani, Deborah A. Stacey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Ontologies are a very important technology in the semantic web. They are an approximate representation and formalization of a domain of discourse in a manner that is both machine and human interpretable. Ontology evaluation therefore, concerns itself with measuring the degree to which the ontology approximates the domain. In data-driven ontology evaluation, the correctness of an ontology is measured agains a corpus of documents about the domain. This domain knowledge is dynamic and evolves over several dimensions such as the temporal and categorical. Current research makes an assumption that is contrary to this notion and hence does not account for the existence of bias in ontology evaluation. This work addresses this gap and proposes two metrics as well as a theoretical framework. It also presents a statistical evaluation of the framework and the associated metrics.

Original languageEnglish
Title of host publicationKEOD 2014 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development
EditorsJoaquim Filipe, Jan Dietz, Joaquim Filipe, David Aveiro
PublisherINSTICC Press
Pages56-66
Number of pages11
ISBN (Electronic)9789897580499
Publication statusPublished - Jan 1 2014
Event6th International Conference on Knowledge Engineering and Ontology Development, KEOD 2014 - Rome, Italy
Duration: Oct 21 2014Oct 24 2014

Other

Other6th International Conference on Knowledge Engineering and Ontology Development, KEOD 2014
CountryItaly
CityRome
Period10/21/1410/24/14

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All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Cite this

Hlomani, H., & Stacey, D. A. (2014). Data-driven diachronic and categorical evaluation of ontologies: Framework, measure, and metrics. In J. Filipe, J. Dietz, J. Filipe, & D. Aveiro (Eds.), KEOD 2014 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development (pp. 56-66). INSTICC Press.