Parameter learning for probabilistic ontologies

Authors:
Fabrizio Riguzzi;Elena Bellodi;Evelina Lamma;Riccardo Zese
Affiliations:
Dipartimento di Matematica e Informatica, University of Ferrara, Ferrara, Italy;Dipartimento di Ingegneria, University of Ferrara, Ferrara, Italy;Dipartimento di Ingegneria, University of Ferrara, Ferrara, Italy;Dipartimento di Ingegneria, University of Ferrara, Ferrara, Italy
Venue:
RR'13 Proceedings of the 7th international conference on Web Reasoning and Rule Systems
Year:
2013

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Abstract

Recently, the problem of representing uncertainty in Description Logics (DLs) has received an increasing attention. In probabilistic DLs, axioms contain numeric parameters that are often difficult to specify or to tune for a human. In this paper we present an approach for learning and tuning the parameters of probabilistic ontologies from data. The resulting algorithm, called EDGE, is targeted to DLs following the DISPONTE approach, that applies the distribution semantics to DLs.