Distance-Based Classification in OWL Ontologies
We propose inductive distance-based methods for instance classification and retrieval in ontologies. Casting retrieval as a classification problem with the goal of assessing the individual class-memberships w.r.t. the query concepts, we propose an extension of the k-Nearest Neighbor algorithm for OWL ontologies based on an epistemic distance measure. The procedure can classify the individuals w.r.t. the known concepts but it can also be used to retrieve individuals belonging to query concepts. Experimentally we show that the behavior of the classifier is comparable with the one of a standard reasoner. Moreover we show that new knowledge (not logically derivable) is induced. It can be suggested to the knowledge engineer for validation, during the ontology population task.
KeywordsDescription Logic Near Neighbor Training Instance Formal Concept Analysis Closed World Assumption
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