A framework for semantic-based similarity measures for ELH-concepts

Authors:
Karsten Lehmann;Anni-Yasmin Turhan
Affiliations:
Optimisation Research Group, NICTA, Australia, Artificial Intelligence Group, Australian National University, Australia;Institute for Theoretical Computer, TU Dresden, Germany
Venue:
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
Year:
2012

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Abstract

Similarity measures for concepts written in Description Logics (DLs) are often devised based on the syntax of concepts or simply by adjusting them to a set of instance data. These measures do not take the semantics of the concepts into account and can thus lead to unintuitive results. It even remains unclear how these measures behave if applied to new domains or new sets of instance data. In this paper we develop a framework for similarity measures for $\mathcal{ E\!L\!H}$-concept descriptions based on the semantics of the DL $\mathcal{ E\!L\!H}$. We show that our framework ensures that the measures resulting from instantiations fulfill fundamental properties , such as equivalence invariance, yet the framework provides the flexibility to adjust measures to specifics of the modelled domain.