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- W2769492078 abstract "The subject-matter of this study is knowledge representation in rule-based knowledge bases. The two following issues will be discussed herein: feature selection as a part of mining knowledge bases from a knowledge engineer’s perspective (it is usually aimed at completeness analysis, consistency of the knowledge base and detection of redundancy and unusual rules) as well as from a domain expert’s point of view (domain expert intends to explore the rules with regard to their optimization, improved interpretation and a view to improve the quality of knowledge recorded in the rules). In this sense, exploration of rules, in order to select the most important knowledge, is based, in a great extent, on the analysis of similarities across the rules and their clusters. Building the representatives for created clusters of rules bases on the analysis of the left-hand sides of this rules and then selection of the best descriptive once. Thus we may treat this approach as a feature selection process." @default.
- W2769492078 created "2017-12-04" @default.
- W2769492078 creator A5088457184 @default.
- W2769492078 date "2017-11-17" @default.
- W2769492078 modified "2023-09-24" @default.
- W2769492078 title "Feature Selection Approach for Rule-Based Knowledge Bases" @default.
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- W2769492078 doi "https://doi.org/10.1007/978-3-319-67588-6_9" @default.
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