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Ontology−Mediated Query Answering over Log−Linear Probabilistic Data

Stefan Borgwardt‚ İsmail İlkan Ceylan and Thomas Lukasiewicz

Abstract

Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack the capability to incorporate implicit background knowledge and, consequently, fail to capture some intuitive query answers. Ontology-mediated query answering is a popular paradigm for encoding commonsense knowledge, which can provide more complete answers to user queries. We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model. We compare our approach to existing proposals, and provide supporting computational results.

Book Title
Proceedings of the 33rd National Conference on Artificial Intelligence‚ AAAI 2019‚ Honolulu‚ Hawaii‚ USA‚ January 27 − February 1‚ 2019
Editor
Pascal Van Hentenryck and Zhi−Hua Zhou
Month
January
Pages
2711–2718
Publisher
AAAI Press
Year
2019