Probabilistic reasoning in DL-lite

Authors:
Raghav Ramachandran;Guilin Qi;Kewen Wang;Junhu Wang;John Thornton
Affiliations:
School of Information and Communication Technology, Griffith University, Australia;School of Computer Science and Engineering, Southeast University, China, State Key Lab for Novel Software Technology, Nanjing University, P.R. China;School of Information and Communication Technology, Griffith University, Australia;School of Information and Communication Technology, Griffith University, Australia;School of Information and Communication Technology, Griffith University, Australia
Venue:
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Year:
2012

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Abstract

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.