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Revisable knowledge discovery in databases
Author(s) -
Narayanan Ajit
Publication year - 1996
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199602)11:2<75::aid-int1>3.0.co;2-v
Subject(s) - computer science , knowledge extraction , structuring , knowledge base , inheritance (genetic algorithm) , expert system , identification (biology) , artificial intelligence , data mining , database , machine learning , information retrieval , biochemistry , chemistry , botany , finance , biology , economics , gene
This article introduces the idea of using nonmonotonic inheritance networks for the storage and maintenance of knowledge discovered in data (revisable knowledge discovery in databases). While existing data mining strategies for knowledge discovery in databases typically involve initial structuring through the use of identification trees and the subsequent extraction of rules from these trees for use in rule‐based expert systems, such strategies have difficulty in coping with additional information which may conflict with that already used for the automatic generation of rules. In the worst case, the entire automatic sequence may have to be repeated. If nonmonotonic inheritance networks are used instead of rules for storing knowledge discovered in databases, additional conflicting information can be inserted directly into such structures, thereby bypassing the need for recompilation. © 1996 John Wiley & Sons, Inc.