Most probable explanations for probabilistic database queries
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 extr...
| Main Authors: | , , |
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| Format: | Conference item |
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IJCAI
2017
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| _version_ | 1826285207790026752 |
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| author | Ceylan, I Borgwardt, S Lukasiewicz, T |
| author2 | Sierra, C |
| author_facet | Sierra, C Ceylan, I Borgwardt, S Lukasiewicz, T |
| author_sort | Ceylan, I |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T01:25:24Z |
| format | Conference item |
| id | oxford-uuid:91cc7bb1-633e-4310-9155-5f28d78f9881 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T01:25:24Z |
| publishDate | 2017 |
| publisher | IJCAI |
| record_format | dspace |
| spelling | oxford-uuid:91cc7bb1-633e-4310-9155-5f28d78f98812022-03-26T23:21:07ZMost probable explanations for probabilistic database queriesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:91cc7bb1-633e-4310-9155-5f28d78f9881Symplectic Elements at OxfordIJCAI2017Ceylan, IBorgwardt, SLukasiewicz, TSierra, CForming 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. |
| spellingShingle | Ceylan, I Borgwardt, S Lukasiewicz, T Most probable explanations for probabilistic database queries |
| title | Most probable explanations for probabilistic database queries |
| title_full | Most probable explanations for probabilistic database queries |
| title_fullStr | Most probable explanations for probabilistic database queries |
| title_full_unstemmed | Most probable explanations for probabilistic database queries |
| title_short | Most probable explanations for probabilistic database queries |
| title_sort | most probable explanations for probabilistic database queries |
| work_keys_str_mv | AT ceylani mostprobableexplanationsforprobabilisticdatabasequeries AT borgwardts mostprobableexplanationsforprobabilisticdatabasequeries AT lukasiewiczt mostprobableexplanationsforprobabilisticdatabasequeries |