Most probable explanations for probabilistic database queries

Conference item

Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.

Authors: Ceylan, I; Borgwardt, S; Lukasiewicz, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IJCAI
• Host title: 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, 19-25 August 2017
• Journal: 26th International Joint Conference on Artificial Intelligence (IJCAI 2017)
• Publication date: 2017-08-19
• Acceptance date: 2017-04-24
• Event location: Melbourne‚ Australia
• DOI: 10.24963/ijcai.2017/132