Probabilistic Reasoning in DL-Lite
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The problem of extending description logics with uncertainty has received significant attention in recent years. In this paper, we investigate a probabilistic extension of DL-Lite, a family of tractable description logics. We first present a new probabilistic semantics for terminological knowledge bases based on the notion of types. The semantics proposed is not capable of handling assertional knowledge. In order to reason with both terminological and assertional probabilistic knowledge, we propose a probabilistic semantics based on a finite semantics for DL-Lite called features. This approach enables us to infer new information from the existing knowledge base by drawing on the inherent relation between a probabilistic TBox and a probabilistic ABox.
PRICAI 2012: Trends in Artificial Intelligence 12th Pacific Rim International Conference on Artificial Intelligence
Artificial Intelligence and Image Processing not elsewhere classified