Authors:
Jidi Zhao
1
;
Harold Boley
2
and
Weichang Du
1
Affiliations:
1
University of New Brunswick, Canada
;
2
National Research Council of Canada, Canada
Keyword(s):
Semantic web, Uncertain knowledge, Description logic, Fuzzy logic, Linear programming.
Related
Ontology
Subjects/Areas/Topics:
Approximate Reasoning and Fuzzy Inference
;
Artificial Intelligence
;
Complex Fuzzy Systems
;
Computational Intelligence
;
Fuzzy Systems
;
Fuzzy Systems Design, Modeling and Control
;
Soft Computing
Abstract:
While applications in different areas have shown the necessity of dealing with uncertain knowledge, Semantic Web techniques based on standard Description Logics do not have such a capability. Motivated by this discrepancy, we introduce an expressive fuzzy description logic, fZSI , which extends the classic Description Logic SI to deal with uncertain knowledge about concepts and roles as well as instances of concepts and roles. In the family of Fuzzy Logics it is semantically based on Zadeh Logic, which naturally interprets uncertain knowledge about concepts and roles as fuzzy sets and fuzzy relations, and interprets uncertain knowledge about instances as elements with degrees of membership. The paper focuses on several reasoning methods for the main reasoning problems in fZSI, including consistency checking, instance range entailment, and f-retrieval problems.