Most Probable Explanations for Probabilistic Database Queries

İsmail İlkan Ceylanİsmail İlkan Ceylan, Stefan BorgwardtStefan Borgwardt, Thomas LukasiewiczThomas Lukasiewicz

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İsmail İlkan Ceylan, Stefan Borgwardt, Thomas Lukasiewicz
Most Probable Explanations for Probabilistic Database Queries
In Carles Sierra, eds., Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), to appear

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KurzfassungAbstract
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.
Projekt:Project: GoAsQ, HAEC
Forschungsgruppe:Research Group: AutomatentheorieAutomata Theory

@inproceedings{CBL2017,
  author    = {{\.{I}}smail {\.{I}}lkan Ceylan and Stefan Borgwardt and Thomas
               Lukasiewicz},
  title     = {Most Probable Explanations for Probabilistic Database Queries},
  editor    = {Carles Sierra},
  booktitle = {Proceedings of the 26th International Joint Conference on
               Artificial Intelligence (IJCAI 2017)},
  year      = {2017}
}