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International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management

IC3K 2014: Knowledge Discovery, Knowledge Engineering and Knowledge Management pp 329–346Cite as

Multiple Dimensions to Data-Driven Ontology Evaluation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 553))

Abstract

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 data-driven 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.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Guelph, Guelph, Canada

    Hlomani Hloman & Deborah A. Stacey

Authors
  1. Hlomani Hloman
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  2. Deborah A. Stacey
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Corresponding author

Correspondence to Hlomani Hloman .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisboa, Portugal

    Ana Fred

  2. Delft University of Technology, Delft, Zuid-Holland, The Netherlands

    Jan L. G. Dietz

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. Henley Business School, University of Reading, Reading, United Kingdom

    Kecheng Liu

  5. INSTICC, Setubal, Portugal

    Joaquim Filipe

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© 2015 Springer International Publishing Switzerland

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Hloman, H., Stacey, D.A. (2015). Multiple Dimensions to Data-Driven Ontology Evaluation. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2014. Communications in Computer and Information Science, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-319-25840-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-25840-9_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25839-3

  • Online ISBN: 978-3-319-25840-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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