Ontology-Mediated Query Answering over Log-Linear Probabilistic Data

Abstract

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.

Probabilistic Databases with an Infinite Open-World Assumption

“…In the context of probabilistic databases we want to emphasize again the impact of the Open-PDB model [14] to our investigations. More recently, it has been proposed to extend OpenPDBs using domain knowledge in the form of ontologies [11,12], yielding more intuitive query results with respect to the open-world assumption in OpenPDBs.…”

onto2problog: A Probabilistic Ontology-Mediated Querying System using Probabilistic Logic Programming

“…First, we want to integrate first-order rewritings into our program natively, which on the one hand exhibited better performance in some of our experiments, but on the other hand are incomplete in general. Second, we want to investigate whether our approach can be extended to different ontology languages, such as those in the Datalog ± family [5]. Finally, it would be interesting to see whether other capabilities of ProbLog, such as learning, can be transferred to the OMQPD setting.…”

Introduction to Probabilistic Ontologies

“…Through our chosen notion of probabilistic ontologies, where some axioms are labelled with a real number between 0 and 1, we have in fact excluded many formalisms which have been-or could be-considered as probabilistic ontology languages as well. Prominently, it leaves out all formalisms based on log-linear interpretations of probabilities [9,55]. In those formalisms, probabilities are not assigned directly, but are rather implicitly encoded in weights, which can be arbitrarily large real numbers.…”