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AIIA
2015
Springer

Learning Probabilistic Ontologies with Distributed Parameter Learning

3 years 1 months ago
Learning Probabilistic Ontologies with Distributed Parameter Learning
We consider the problem of learning both the structure and the parameters of Probabilistic Description Logics under DISPONTE. DISPONTE (“DIstribution Semantics for Probabilistic ONTologiEs”) adapts the distribution semantics for Probabilistic Logic Programming to Description Logics. The system LEAP for “LEArning Probabilistic description logics” learns both the structure and the parameters of DISPONTE knowledge bases (KBs) by exploiting the algorithms CELOE and EDGE. The former stands for “Class Expression Learning for Ontology Engineering” and it is used to generate good candidate axioms to add to the KB, while the latter learns the probabilistic parameters and evaluates the KB. EDGE for “Em over bDds for description loGics paramEter learning” is an algorithm for learning the parameters of probabilistic ontologies from data. In order to contain the computational cost, a distributed version of EDGE called EDGEMR was developed. EDGEMR exploits the MapReduce (MR) strategy...
Giuseppe Cota, Riccardo Zese, Elena Bellodi, Eveli
Added 14 Apr 2016
Updated 14 Apr 2016
Type Journal
Year 2015
Where AIIA
Authors Giuseppe Cota, Riccardo Zese, Elena Bellodi, Evelina Lamma, Fabrizio Riguzzi
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