Author:
Hendrik Decker
Affiliation:
Universidad Politécnica de Valencia, Spain
Keyword(s):
Inconsistency Tolerance, Datalog, Integrity.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Decision Support Systems
;
Enterprise Information Systems
;
Expert Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Symbolic Systems
Abstract:
Inconsistency tolerance is widely discussed and accepted in the scientific community of knowledge engineering. From a principled, theoretical point of view, however, the fundamental conflict of sound reasoning with unsound data has remained largely unresolved. The vast majority of applications that need inconsistency tolerance either does not care about a firm theoretical underpinning, or recurs on non-standard logics, or superficially refers to well-established classical foundations. We argue that hardly any of these paradigms will survive in the long run. We defend the position that datalog (Abiteboul et al., 1995), including integrity constraints, is a viable candidate for a sound and robust foundation of inconsistency-tolerant knowledge engineering. We line our argument by a propaedeutic glance at the history of issues related to inconsistency.