Learning Vague Concepts for the Semantic Web
This addresses ontology interoperability and evolution issues for Semantic Web researchers, but it is incremental as it builds on existing frameworks for vagueness.
The paper tackles the problem of vague concepts in formal ontologies, which cause inconsistencies during updates, by proposing a method to detect and repair these inconsistencies locally.
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.