{"type":"ScholarlyArticle","title":"Most Probable Explanations for Probabilistic Database Queries","authors":[{"name":"İsmail İlkan Ceylan","slug":"ismail-ilkan-ceylan","given":"İsmail İlkan","family":"Ceylan","affiliation":"Faculty of Computer Science Technische Universität Dresden , Germany"},{"name":"Stefan Borgwardt","slug":"stefan-borgwardt","given":"Stefan","family":"Borgwardt","affiliation":"Faculty of Computer Science Technische Universität Dresden , Germany"},{"name":"Thomas Lukasiewicz","slug":"thomas-lukasiewicz","given":"Thomas","family":"Lukasiewicz","affiliation":"Department of Computer Science University of Oxford , UK"}],"abstract":"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.","doi":"10.24963/ijcai.2017/132","url":"https://ijcai.org/proceedings/2017/132","pdf":"https://ijcai.org/proceedings/2017/0132.pdf","markdown":"https://ijcai.org/proceedings/2017/132.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/132","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":132,"pages":{"first":950,"last":956},"keywords":["Knowledge Representation, Reasoning, and Logic: Description Logics and Ontologies","Knowledge Representation, Reasoning, and Logic: Computational Complexity of Reasoning","Uncertainty in AI: Exact Probabilistic Inference","Uncertainty in AI: Relational Inference"],"videos":[],"references":[{"position":1,"raw":"[Baader et al., 2003] Franz Baader, Diego Calvanese, Deborah L. 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