Conceptual clustering in knowledge organization
Knowledge organization is a very important step in building an expert system. The problem is how to organize knowledge into a conceptual structure and thus make it complete, concise, and consistent. In this paper, concepts used in knowledge description are divided into tangible ones and intermediate...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 7(1985), 5 vom: 01. Mai, Seite 592-8 |
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| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
1985
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Knowledge organization is a very important step in building an expert system. The problem is how to organize knowledge into a conceptual structure and thus make it complete, concise, and consistent. In this paper, concepts used in knowledge description are divided into tangible ones and intermediate ones depending on whether or not they appear in the input or the output of the system. Intermediate concepts and their relationships with tangible concepts are subjected to changes. A distance measure for rules and an algorithm for conceptual clustering are described. New intermediate concepts are generated using this algorithm. A few new concepts may replace a large number of old relationships and also generate new rules for the system. An experiment on traditional Chinese medicine shows that the proposed method produces results similar to those generated by experts |
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| Beschreibung: | Date Completed 02.10.2012 Date Revised 12.11.2019 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |