Multi-level Rule Discovery from Knowledge Bases
نویسنده
چکیده
Data is not the only resource that can lead to knowledge discovery. Knowledge itself in the form of an expert system or output from data mining can be further manipulated to produce multi-level knowledge. This can be used to suggest more interesting concepts and improve understanding and querying of the original data and/or knowledge. Knowledge has different properties to data, the most obvious and problematic being that only one of each rule exists. This paper considers a solution to this problem and the possible benefits of mining knowledge and describes a technique for reorganizing knowledge and discovering higher-level concepts in the knowledge base. The technique we offer can be used in two ways. Perform knowledge acquisition to acquire a rule base, we recommend an incremental technique known as Ripple Down Rules, or use data mining techniques to develop a first round of lowlevel rules. Once knowledge exists in propositional form, Formal Concept Analysis is applied to the rules to develop an abstraction hierarchy from which multi-level rules can be extracted. The user is able to explore the knowledge at and across any of the levels of abstraction to provide a much richer picture of the knowledge and understanding of the domain.
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تاریخ انتشار 2001