- Titel
- Most Probable Explanations for Probabilistic Database Queries
- Untertitel
- Extended Version
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-887797
- Schriftenreihe
- LTCS-Report
- Bandnummer
- 17-11
- Erstveröffentlichung
- 2017
- DOI
- https://doi.org/10.25368/2023.220
- Abstract (EN)
- 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.
- Andere Ausgabe
- Zuerst erschienen in „Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence”.
DOI: 10.24963/ijcai.2017/132 - Freie Schlagwörter (DE)
- Probabilistische Datenbanken, Erklärungen, Anfragebeantwortung
- Freie Schlagwörter (EN)
- probabilistic databases, explanations, query answering
- Klassifikation (DDC)
- 004
- Klassifikation (RVK)
- ST 136
- Herausgeber (Institution)
- Technische Universität Dresden
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft Sonderforschungsbereich 912: Highly Adaptive Energy-Efficient Computing
(SFB 912: HAEC) - Deutsche Forschungsgemeinschaft Rollenbasierte Software-Infrastrukturen für durchgängig-kontextsensitive Systeme
(RoSI)
ID: GRK 1907 - Deutsche Forschungsgemeinschaft Generating & AnSwering Ontological Queries
(GoAsQ)
ID: BA 1122/19-1 - Engineering and Physical Sciences Research Council Probabilistic Ontological Query Answering on the Web
(PrOQAW)
ID: EP/J008346/1 - Engineering and Physical Sciences Research Council Bridging Databases and Ontologies
(DBOnto)
ID: EP/L012138/1 - Engineering and Physical Sciences Research Council Value Added Data Systems -- Principles and Architecture
(VADA)
ID: EP/M025268/1 - Engineering and Physical Sciences Research Council The Alan Turing Institute
ID: EP/N510129/1 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-887797
- Veröffentlichungsdatum Qucosa
- 28.12.2023
- Dokumenttyp
- Bericht
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0