Most Probable Explanations for Probabilistic Database Queries
نویسندگان
چکیده
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.
منابع مشابه
Most Probable Explanations for Probabilistic Database Queries (Extended Abstract)
Probabilistic databases (PDBs) have been widely studied in the literature, as they form the foundations of large-scale probabilistic knowledge bases like NELL and Google’s Knowledge Vault. 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...
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Probabilistic databases (PDBs) have been widely studied in the literature, as they form the foundations of large-scale probabilistic knowledge bases like NELL and Google’s Knowledge Vault. 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...
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