This paper explores how knowledge in the form of propositions in an expert system can be used as input into data mining. The output is multi-level knowledge which can be used to provide structure, suggest interesting concepts, improve understanding and support querying of the original knowledge. Appropriate algorithms for mining knowledge must take into account the peculiar features of knowledge which distinguish it from data. The most obvious and problematic distinction is that only one of each rule exists. This paper introduces the possible benefits of mining knowledge and describes a technique for reorganizing knowledge and discovering higher-level concepts in the knowledge base. The rules input may have been acquired manually (we describe a simple technique known as Ripple Down Rules for this purpose) or automatically using an existing data mining technique. In either case, once the 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.
|Number of pages||18|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2003|