Computer Science

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

Abstract: Increasingly sophisticated knowledge requires an inclusive knowledge representation that facilitates the integration of independently-generated information arising from different kind of efforts. The use of semantic web technologies addresses the reality of diverse interests on an ontology merging and supports knowledge discovery over that.
Significant research efforts in the Semantic Web are recently directed towards representing and reasoning with uncertainty and vagueness in ontologies. It is a challenge for any Knowledge Base reasoning to manage ubiquitous uncertain ontology as well as updating with uncertain updating times, while achieving acceptable service levels at minimum computational cost. Uncertain updating times of ontologies and managing probabilistic uncertainty, possibilistic uncertainty, and vagueness in expressive description logics for the Semantic Web are well recognized to be one of the primary drivers of the costs in many ontology development.
This paper proposes an application-independent merging ontologies using the notions of temporal proposition, event, agent and role that can be used to specify any open interaction system towards merging different kind of ontologies.
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.

Keywords: Ontology evolution, mapping, merging, and alignment; Semantic coordination, integration, matching and interoperability; Semantic networks; Knowledge management and reasoning on the Web; Reasoning over ontologies.

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