Building Knowledge Scouts Using KGL Metalanguage

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dc.contributor.authorMichalski, Ryszard S.
dc.contributor.authorKaufman, Kenneth A.
dc.date.accessioned2006-11-03T18:17:03Z
dc.date.available2006-11-03T18:17:03Z
dc.date.issued2000
dc.description.abstractKnowledge 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.sponsorshipThe 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.
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dc.format.mimetypeapplication/postscript
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dc.identifier.citationMichalski, R. S. and Kaufman, K., "Building Knowledge Scouts Using KGL Metalanguage," Fundamenta Informaticae, vol. 40, pp 433-447, 2000.
dc.identifier.urihttps://hdl.handle.net/1920/1464
dc.language.isoen_US
dc.relation.ispartofseriesP 00-6
dc.subjectData mining
dc.subjectKnowledge discovery
dc.subjectKnowledge scouts
dc.subjectInductive databases
dc.subjectKnowledge visualization
dc.subjectKnowledge generation language
dc.subjectAssociation graphs
dc.subjectAttributional calculus
dc.titleBuilding Knowledge Scouts Using KGL Metalanguage
dc.typeArticle

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