Completion-based generalization inferences for the description logic ELOR with subjective probabilities

Description Logics (DLs) are a well-established family of knowledge representation formalisms. One of its members, the DL ELOR has been successfully used for representing knowledge from the bio-medical sciences, and is the basis for the OWL 2 EL profile of the standard ontology language for the Semantic Web. Reasoning in this DL can be performed in polynomial time through a completion-based algorithm. In this paper we study the logic Prob-ELOR, that extends ELOR with subjective probabilities, and present a completion-based algorithm for polynomial time reasoning in a restricted version, Prob-ELOR^c_01, of Prob-ELOR. We extend this algorithm to computation algorithms for approximations of (i) the most specific concept, which generalizes a given individual into a concept description, and (ii) the least common subsumer, which generalizes several concept descriptions into one. Thus, we also obtain methods for these inferences for the OWL 2 EL profile. These two generalization inferences are fundamental for building ontologies automatically from examples. The feasibility of our approach is demonstrated empirically by our prototype system GEL