Affiliations: Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030-4444, USA. michalski@gmu.edu
Note: [] Also with Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland Address for correspondence: Machine Learning and Inference Laboratory, George Mason University Fairfax, VA 22030-4444, USA
Abstract: 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: (1) attributional rules, which are rules in attributional calculus—a logic-based language between prepositional 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 this methodology for deriving knowledge from databases.
Keywords: data mining, knowledge discovery, knowledge scouts, inductive databases, knowledge visualization, knowledge generation language, association graphs, attributional calculus
