- Titel
- Ontology-Mediated Query Answering over Log-Linear Probabilistic Data
- Untertitel
- Extended Version
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-887803
- Schriftenreihe
- LTCS-Report
- Bandnummer
- 19-10
- Erstveröffentlichung
- 2019
- DOI
- https://doi.org/10.25368/2023.221
- Abstract (EN)
- Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack the capability to incorporate implicit background knowledge and, consequently, fail to capture some intuitive query answers. Ontology-mediated query answering is a popular paradigm for encoding commonsense knowledge, which can provide more complete answers to user queries. We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model. We compare our approach to existing proposals, and provide supporting computational results.
- Andere Ausgabe
- Zuerst erschienen in „Proceedings of the AAAI Conference on Artificial Intelligence”.
DOI: 10.1609/aaai.v33i01.33012711 - Freie Schlagwörter (DE)
- Probabilistische Datenbanken, Markov-Logik-Netzwerke, Anfragebeantwortung
- Freie Schlagwörter (EN)
- probabilistic databases, Markov logic networks, query answering
- Klassifikation (DDC)
- 004
- Klassifikation (RVK)
- ST 136
- Herausgeber (Institution)
- Technische Universität Dresden
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft Generating & AnSwering Ontological Queries
(GoAsq)
ID: BA 1122/19-1 - Engineering and Physical Sciences Research Council The Alan Turing Institute
ID: EP/N510129/1 - Engineering and Physical Sciences Research Council Realistic Data Models and Query Compilation for Large-Scale Probabilistic Databases
(RealPDBs)
ID: EP/R013667/1 - 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 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-887803
- Veröffentlichungsdatum Qucosa
- 28.12.2023
- Dokumenttyp
- Bericht
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0