Dynamic Bayesian Ontology Languages
This work addresses the need for handling dynamic uncertainty in ontology languages, which is incremental as it builds on existing formalisms by adding temporal evolution.
The paper tackles the problem of static probabilistic assumptions in ontology languages by introducing a general approach to incorporate time-evolving uncertainty using dynamic Bayesian networks, enabling effective reasoning through the integration of original language reasoning and dynamic Bayesian inferences.
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.