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Structural Distance between \(\mathcal{EL}^{+}\) Concepts

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7080))

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Abstract

The inexpressive Description Logics in the \(\mathcal{EL}\) family have been successful mainly due to their tractability of standard reasoning tasks like subsumption, adoption in modeling of biomedical ontologies, and standardization as an OWL 2 EL profile. This paper proposes two enhanced subsumption algorithms that not only test whether a particular subsumption holds but also give a numeric indicator showing the structural distance between the two concepts in the subsumption relationship. Structural distance is defined in terms of the effort required to obtain the subsumption in question. Both algorithms extend the standard subsumption algorithm for \(\mathcal{EL}^{+}\) by an axiom labeling technique, originally proposed for finding justifications.

This work is partially funded by Thailand Research Fund under grant MRG5380245 and the NRU project of Thailand Office for Higher Education Commission.

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Authors and Affiliations

  1. School of Information and Computer Technology, Sirindhorn International Institute of Technology, Thammasat University, Thailand

    Boontawee Suntisrivaraporn

Authors
  1. Boontawee Suntisrivaraporn
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Editors and Affiliations

  1. Faculty of Informatics, Mahasarakham University, Khamreing, 44150, Kantarawichai, Mahasarakham, Thailand

    Chattrakul Sombattheera

  2. Department of Computers and Information Science, University of Hyderabad, 500046, Hyderabad, Andhra Pradesh, India

    Arun Agarwal

  3. Department of Computer and Information Sciences, University of Hyderabad, 500046, Hyderabad, India

    Siba K Udgata

  4. School of Information Technology, King Mongkut’s University of Technology, Thonburi, Bangkok, Thailand

    Kittichai Lavangnananda

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Suntisrivaraporn, B. (2011). Structural Distance between \(\mathcal{EL}^{+}\) Concepts. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2011. Lecture Notes in Computer Science(), vol 7080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_9

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  • DOI: https://doi.org/10.1007/978-3-642-25725-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25724-7

  • Online ISBN: 978-3-642-25725-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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