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- 159 creator krzysztof-janowicz.
- 159 creator marc-wilkes.
- 159 type InProceedings.
- 159 label "SIM-DL_A: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-Concept to Inter-Instance Similarity".
- 159 sameAs 159.
- 159 abstract "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-DL_A, 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 tableau 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.".
- 159 hasAuthorList authorList.
- 159 hasTopic Data_mining.
- 159 hasTopic Ontology_%28computer_science%29.
- 159 hasTopic Ontology_alignment.
- 159 hasTopic Query.
- 159 hasTopic Search_engine.
- 159 hasTopic Semantic_Web.
- 159 hasTopic Visualization.
- 159 isPartOf proceedings.
- 159 keyword "description logics".
- 159 keyword "information retrieval".
- 159 keyword "semantic similarity".
- 159 title "SIM-DL_A: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-Concept to Inter-Instance Similarity".