Michalski, Ryszard S.Kaufman, Kenneth A.2006-11-032006-11-032000Michalski, R. S. and Kaufman, K., "Building Knowledge Scouts Using KGL Metalanguage," Fundamenta Informaticae, vol. 40, pp 433-447, 2000.https://hdl.handle.net/1920/1464Knowledge 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.3188 bytes227525 bytes68906 bytestext/xmlapplication/postscriptapplication/pdfen-USData miningKnowledge discoveryKnowledge scoutsInductive databasesKnowledge visualizationKnowledge generation languageAssociation graphsAttributional calculusArticle