A Framework for Semantic-based Similarity Measures for ELH-Concepts

Karsten LehmannKarsten Lehmann, Anni-Yasmin TurhanAnni-Yasmin Turhan

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Karsten Lehmann, Anni-Yasmin Turhan
A Framework for Semantic-based Similarity Measures for ELH-Concepts
In Luis Fariñas del Cerro and Andreas Herzig and Jérôme Mengin, eds., Proceedings of the 13th European Conference on Logics in Artificial Intelligence, Lecture Notes in Artificial Intelligence, 307-319, 2012. Springer

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KurzfassungAbstract
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 ELH-concept descriptions based on the semantics of the DL ELH. 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.
Forschungsgruppe:Research Group: AutomatentheorieAutomata Theory

The final publication is available at Springer.

@inproceedings{ LeTu-Jelia12,
  author = {Karsten {Lehmann} and Anni-Yasmin {Turhan}},
  booktitle = {Proceedings of the 13th European Conference on Logics in Artificial Intelligence},
  editor = {Luis Fari{\~n}as del {Cerro} and Andreas {Herzig} and J{\'e}r{\^o}me {Mengin}},
  pages = {307--319},
  publisher = {Springer Verlag},
  series = {Lecture Notes in Artificial Intelligence},
  title = {A Framework for Semantic-based Similarity Measures for {$\mathcal{ELH}$}-Concepts},
  year = {2012},
}