Building knowledge scouts using KGL metalanguage

• Autorzy: Michalski R. , Kaufman K.A.
• Języki publikacji: EN

Abstrakty

EN

Knowledge scouts are software agents that autonomously synthesize user-oriented knowledge (target knowledge) from information present in 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: attributional rules, which are rules in attributional calculus-a logic-based language between propositional and predicate calculus, and 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 this methodology for deriving knowledge from databases.

Słowa kluczowe

EN

data mining; knowledge discovery; knowledge scouts; inductive databases; knowledge visualization; knowledge generation language; association graphs; attributional calculus

• Wydawca: Polskie Towarzystwo Matematyczne
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2000
• Tom: Vol. 41, Nr 4
• Strony: 433--447
• Opis fizyczny: bibliogr. 20 poz.

Twórcy

autor

Michalski R.

autor

Kaufman K.A.

• Machine Learning and Inference Laboratory, George Mason University Fairfax, VA 22030-4444, USA,