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 param- Eter 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. Ex- periments on various domains show that EDGEMR signicantly reduces EDGE running time.
Distributed Parameter Learning for Probabilistic Ontologies / Cota, Giuseppe; Zese, Riccardo; Bellodi, Elena; Riguzzi, Fabrizio; Lamma, Evelina. - STAMPA. - 9575:(2016), pp. 30-45. (Intervento presentato al convegno 25th International Conference on Inductive Logic Programming: tenutosi a Kyoto, Japan nel August 20-22, 2015) [10.1007/978-3-319-40566-7_3].
Distributed Parameter Learning for Probabilistic Ontologies
COTA, Giuseppe;
2016-01-01
Abstract
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 param- Eter 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. Ex- periments on various domains show that EDGEMR signicantly reduces EDGE running time.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
