SIM-DLA: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-concept to Inter-instance Similarity

  • Krzysztof Janowicz
  • Marc Wilkes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5554)


While semantic similarity plays a crucial role for human categorization and reasoning, computational similarity measures have also been applied to fields such as semantics-based information retrieval or ontology engineering. Several measures have been developed to compare concepts specified in various description logics. In most cases, these measures are either structural or require a populated ontology. Structural measures fail with an increasing expressivity of the used description logic, while several ontologies, e.g., geographic feature type ontologies, are not populated at all. In this paper, we present an approach to reduce inter-concept to inter-instance similarity and thereby avoid the canonization problem of structural measures. The novel approach, called SIM-DLA, reuses existing similarity functions such as co-occurrence or network measures from our previous SIM-DL measure. The required instances for comparison are derived from the completion tree of a slightly modified DL-tableau algorithm as used for satisfiability checking. Instead of trying to find one (clash-free) model, the new algorithm generates a set of proxy individuals used for comparison. The paper presents the algorithm, alignment matrix, and similarity functions as well as a detailed example.


Semantic Similarity Description Logic Target Concept Primitive Concept Ontology Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Krzysztof Janowicz
    • 1
  • Marc Wilkes
    • 1
  1. 1.Institute for GeoinformaticsUniversity of MuensterGermany

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