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Towards Ontology Refinement by Combination of Machine Learning and Attribute Exploration

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Knowledge Engineering and Knowledge Management (EKAW 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8982))

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Abstract

We propose a new method for knowledge acquisition and ontology refinement for the Semantic Web. The method is based on a combination of the attribute exploration algorithm from the formal concept analysis and active learning approach to machine learning classification task. It enables utilization of Linked Data during the process of an ontology refinement in a manner that it is possible to use remote SPARQL endpoints. We also report on a preliminary experimental evaluation and argue that our method is reasonable and useful.

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Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60-965, Poznan, Poland

    Jedrzej Potoniec

Authors
  1. Jedrzej Potoniec
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Correspondence to Jedrzej Potoniec .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Patrick Lambrix

  2. Aalto University, Espoo, Finland

    Eero Hyvönen

  3. Linköping University, Linköping, Sweden

    Eva Blomqvist

  4. STLab ISTC-CNR, Rome, Italy

    Valentina Presutti

  5. Southeast University, Nanjing, Jiangsu, China

    Guilin Qi

  6. University of Manchester, Manchester, United Kingdom

    Uli Sattler

  7. Indiana University Bloomington, Bloomington, Indiana, USA

    Ying Ding

  8. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

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Potoniec, J. (2015). Towards Ontology Refinement by Combination of Machine Learning and Attribute Exploration. In: Lambrix, P., et al. Knowledge Engineering and Knowledge Management. EKAW 2014. Lecture Notes in Computer Science(), vol 8982. Springer, Cham. https://doi.org/10.1007/978-3-319-17966-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-17966-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17965-0

  • Online ISBN: 978-3-319-17966-7

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