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A Fix-Point Semantics for Rule-Base Anomalies

A Fix-Point Semantics for Rule-Base Anomalies

Du Zhang
Copyright: © 2007 |Volume: 1 |Issue: 4 |Pages: 12
ISSN: 1557-3958|EISSN: 1557-3966|ISSN: 1557-3958|EISBN13: 9781615201969|EISSN: 1557-3966|DOI: 10.4018/jcini.2007100102
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MLA

Zhang, Du. "A Fix-Point Semantics for Rule-Base Anomalies." IJCINI vol.1, no.4 2007: pp.14-25. http://doi.org/10.4018/jcini.2007100102

APA

Zhang, D. (2007). A Fix-Point Semantics for Rule-Base Anomalies. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 1(4), 14-25. http://doi.org/10.4018/jcini.2007100102

Chicago

Zhang, Du. "A Fix-Point Semantics for Rule-Base Anomalies," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 1, no.4: 14-25. http://doi.org/10.4018/jcini.2007100102

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Abstract

A crucial component of an intelligent system is its knowledge base (KB) that contains knowledge about a problem domain. KB development involves domain analysis, context space definition, ontological specification, and knowledge acquisition, codification, and verification. KB anomalies can affect the correctness and performance of an intelligent system. In this article, we describe a fix-point semantics for a KB that is based on a multi-valued logic. We then use the fix-point semantics to provide formal definitions for four types of KB anomalies: (1) inconsistency, (2) redundancy, (3) incompleteness, and (4) circularity. We believe such formal definitions of KB anomalies will help pave the way for a more effective KB verification process.

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