Learning Vague Concepts for the Semantic Web

Abstract

Ontologies can be a powerful tool for structuring knowledge, and they are currently the subject of extensive research. Updating the contents of an ontology or improving its interoperability with other ontologies is an important but difficult process. In this paper, we focus on the presence of vague concepts, which are pervasive in natural language, within the framework of formal ontologies. We will adopt a framework in which vagueness is captured via numerical restrictions that can be automatically adjusted. Since updating vague concepts, either through ontology alignment or ontology evolution, can lead to inconsistent sets of axioms, we define and implement a method to detecting and repairing such inconsistencies in a local fashion.