Ontology Temporal Evolution for Multi-Entity Bayesian Networks under Exogenous and Endogenous Semantic Updating

TL;DR

This paper introduces a probabilistic framework using Multi-Entity Bayesian Networks and SWRL rules to manage and monitor the temporal evolution of uncertain ontologies in open systems, enhancing reasoning under uncertainty.

Contribution

It presents a novel approach combining Bayesian Networks, SWRL rules, and Java implementation for dynamic ontology merging and evolution monitoring.

Findings

Effective management of uncertain ontology evolution

Dynamic monitoring of exogenous and endogenous updates

Probabilistic framework improves reasoning accuracy

Abstract

It is a challenge for any Knowledge Base reasoning to manage ubiquitous uncertain ontology as well as uncertain updating times, while achieving acceptable service levels at minimum computational cost. This paper proposes an application-independent merging ontologies for any open interaction system. A solution that uses Multi-Entity Bayesan Networks with SWRL rules, and a Java program is presented to dynamically monitor Exogenous and Endogenous temporal evolution on updating merging ontologies on a probabilistic framework for the Semantic Web.