Building Knowledge Scouts Using KGL Metalanguage




Michalski, Ryszard S.
Kaufman, Kenneth A.

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Knowledge scouts are software agents that autonomously search for and synthesize user-oriented knowledge (target knowledge) in large local or distributed databases. A knowledge generation metalanguage, KGL, is used to creating scripts defining such knowledge scouts. Knowledge scouts operate in an inductive database, by which we mean a database system in which conventional data and knowledge management operators are integrated with a wide range of data mining and inductive inference operators. Discovered knowledge is represented in two forms: (1) attributional rules, which are rules in attributional calculus -- a logic-based language between propositional and predicate calculus, and (2) association graphs, which graphically and abstractly represent relations expressed by the rules. These graphs can depict multi-argument relationships among different concepts, with a visual indication of the relative strength of each dependency. Presented ideas are illustrated by two simple knowledge scouts, one that seeks relations among lifestyles, environmental conditions, symptoms and diseases in a large medical database, and another that searches for patterns of children's behavior in the National Youth Survey database. The preliminary results indicate a high potential utility of the presented methodology as a tool for deriving knowledge from databases.



Data mining, Knowledge discovery, Knowledge scouts, Inductive databases, Knowledge visualization, Knowledge generation language, Association graphs, Attributional calculus


Michalski, R. S. and Kaufman, K., "Building Knowledge Scouts Using KGL Metalanguage," Fundamenta Informaticae, vol. 40, pp 433-447, 2000.