Multiple dimensions to data-driven ontology evaluation

Hlomani Hloman, Deborah A. Stacey

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

4 Citations (Scopus)


This chapter explores the multiple dimensions to data-driven ontology evaluation. Theoretically and empirically it suggests two ontology evaluation metrics - temporal bias and category bias, as well as an evaluation approach that are geared towards accounting for bias in datadriven ontology evaluation. 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 chapter addresses this gap through experimentation and statistical evaluation.

Original languageEnglish
Title of host publicationKnowledge Discovery, Knowledge Engineering and Knowledge Management - 6th International Joint Conference, IC3K 2014, Revised Selected Papers
EditorsJoaquim Filipe, Jan L.G. Dietz, Joaquim Filipe, Jan L.G. Dietz, Kecheng Liu, David Aveiro, Ana Fred, David Aveiro, Ana Fred, Kecheng Liu
PublisherSpringer Verlag
Number of pages18
ISBN (Print)9783319258393, 9783319258393
Publication statusPublished - 2015
Event6th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2014 - Rome, Italy
Duration: Oct 21 2014Oct 24 2014

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference6th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2014

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics


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