ISSN:
1573-0409
Keywords:
Semantic variations
;
learning
;
classification
;
knowledge-based systems
;
artificial intelligence
;
conceptual knowledge acquisition
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract The notion of concept based on the semantics of objects is defined and illustrated. An underlying thread connecting a subset of concepts is identified. This class of concepts, called the Conceptual Transformer is defined and illustrated with real-world examples. This class finds a natural application in any area where objects can be characterized by functionality. Some interesting application areas are knowledge classification, manufacturing automation, and pattern synthesis. The salient features of this class are elaborated and a knowledge structure for representing concepts is proposed. The effect of these transformers on knowledge-directed classification, which results in the formation of virtual clusters, is examined in detail. We make use of examples from real life to bring out the efficacy of the proposed transformerbased, concept-directed classification.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00247914
