Ontology-Mediated Query Answering over Log-Linear Probabilistic Data

  • Stefan Borgwardt TU Dresden
  • İsmail İlkan Ceylan Univesity of Oxford
  • Thomas Lukasiewicz University of Oxford


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.

AAAI Technical Track: Knowledge Representation and Reasoning