Dynamic Bayesian Ontology Languages

TL;DR

This paper introduces a framework that integrates ontology languages with dynamic Bayesian networks to model and reason about time-evolving uncertainty, extending static probabilistic ontologies for dynamic scenarios.

Contribution

It presents a novel approach combining ontology reasoning with dynamic Bayesian networks to handle temporal uncertainty in knowledge bases.

Findings

Effective reasoning in dynamic probabilistic ontologies demonstrated

Framework supports inference over time-evolving uncertain knowledge

Extends static probabilistic ontologies to dynamic contexts

Abstract

Many formalisms combining ontology languages with uncertainty, usually in the form of probabilities, have been studied over the years. Most of these formalisms, however, assume that the probabilistic structure of the knowledge remains static over time. We present a general approach for extending ontology languages to handle time-evolving uncertainty represented by a dynamic Bayesian network. We show how reasoning in the original language and dynamic Bayesian inferences can be exploited for effective reasoning in our framework.