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Building Knowledge Scouts Using KGL Metalanguage

Show simple item record Michalski, Ryszard S. Kaufman, Kenneth A. 2006-11-03T18:17:03Z 2006-11-03T18:17:03Z 2000 en_US
dc.identifier.citation Michalski, R. S. and Kaufman, K., "Building Knowledge Scouts Using KGL Metalanguage," Fundamenta Informaticae, vol. 40, pp 433-447, 2000. en_US
dc.description.abstract 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.
dc.description.sponsorship The authors thank Jim Logan for providing the American Cancer Society database and discussing experiments done in Study 1. This research was conducted in the Machine Learning and Inference Laboratory at George Mason University under partial support from the National Science Foundation under Grants No. IIS-0012121, IIS-9904078 and IRI-9510644. en_US
dc.format.extent 3188 bytes
dc.format.extent 227525 bytes
dc.format.extent 68906 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 00-6 en_US
dc.subject data mining en_US
dc.subject knowledge discovery en_US
dc.subject knowledge scouts en_US
dc.subject inductive databases en_US
dc.subject knowledge visualization en_US
dc.subject knowledge generation language en_US
dc.subject association graphs en_US
dc.subject attributional calculus en_US
dc.title Building Knowledge Scouts Using KGL Metalanguage en_US
dc.type Article en_US

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