Distributed Parameter Learning for Probabilistic Ontologies

RRepresenting uncertainty in Description Logics has recently received an increasing attention because of its potential to model real world domains. EDGE, for “Em over bDds for description loGics para mEter learning”, is an algorithm for learning the parameters of probabilistic ontologies from data. However, the computational cost of this algorithm is significant since it may take hours to complete an execution. In this paper we present EDGEMR, a distributed version of EDGE that exploits the MapReduce strategy by means of the Message Passing Interface. Experiments on various domains show that EDGEMR significantly reduces EDGE running time.