Distributed Parameter Learning for Probabilistic Ontologies

Representing 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 paramEter learning” is an algorithm for learning the parameters of probabilistic ontologies from data. However, the computational cost of this algorithm is high since it often takes hours to complete an execution. In this paper we present EDGE, a distributed version of EDGE that exploits the MapReduce strategy by means of the Message Passing Interface. Experiments on various domains show that EDGE significantly reduces EDGE running time.