Seeking Knowledge in the Deluge of Facts

Article type: Research Article

Authors: Michalski, Ryszard S.

Affiliations: George Mason University, Fairfax, VA and, Institute for Computer Science, Polish Academy of Sciences, Warsaw, Poland. michalski@gmu.edu

Abstract: An enormous proliferation of computer technology in modern societies has produced a severe information overload. The navigation through the masses of available information in order to derive desired knowledge is becoming increasingly difficult. This creates a demand for intelligent systems capable of assisting data analysts in extracting goal-oriented knowledge from large volumes of data. This paper presents a multistrategy methodology and a system, INLEN, for knowledge discovery in large relational databases. The system integrates data base, knowledge base and machine learning technologies. It offers a data analyst an integrated interface and a wide range of knowledge generation operators, as described in the Inferential Theory of Learning. Presented ideas are illustrated by results from experiments with INLEN.

DOI: 10.3233/FI-1997-303405

Journal: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 283-297, 1997