Authors:
Hlomani Hlomani
and
Deborah A. Stacey
Affiliation:
University of Guelph, Canada
Keyword(s):
Ontology, Ontology Evaluation, Metric, Measure, Data-drive Ontology Evaluation, Ontology Evaluation Framework.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaboration and e-Services
;
Data Engineering
;
e-Business
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Reengineering
;
Knowledge Representation
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Ontology Sharing and Reuse
;
Semantic Web
;
Soft Computing
;
Symbolic Systems
Abstract:
Ontologies are a very important technology in the semantic web. They are an approximate representation and formalization of a domain of discourse in a manner that is both machine and human interpretable. Ontology evaluation therefore, concerns itself with measuring the degree to which the ontology approximates the domain. In data-driven ontology evaluation, the correctness of an ontology is measured agains a corpus of documents about the domain. This domain knowledge is dynamic and evolves over several dimensions such as the temporal and categorical. Current research makes an assumption that is contrary to this notion and hence does not account for the existence of bias in ontology evaluation. This work addresses this gap and proposes two metrics as well as a theoretical framework. It also presents a statistical evaluation of the framework and the associated metrics.