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Mining for knowledge in databases: The INLEN architecture, initial implementation and first results

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Abstract

The architecture of an intelligent multistrategy assistant for knowledge discovery from facts, INLEN, is described and illustrated by an exploratory application. INLEN integrates a database, a knowledge base, and machine learning methods within a uniform user-oriented framework. A variety of machine learning programs are incorporated into the system to serve as high-levelknowledge generation operators (KGOs). These operators can generate diverse kinds of knowledge about the properties and regularities existing in the data. For example, they can hypothesize general rules from facts, optimize the rules according to problem-dependent criteria, determine differences and similarities among groups of facts, propose new variables, create conceptual classifications, determine equations governing numeric variables and the conditions under which the equations apply, deriving statistical properties and using them for qualitative evaluations, etc. The initial implementation of the system, INLEN 1b, is described, and its performance is illustrated by applying it to a database of scientific publications.

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Authors and Affiliations

  1. Center for Artificial Intelligence, George Mason University, 22030, Fairfax, VA

    R. S. Michalski, L. Kerschberg, K. A. Kaufman & J. S. Ribeiro

  2. The Analytic Sciences Corporation, 12100 Sunset Hills Road, 22090, Reston, VA

    J. S. Ribeiro

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  1. R. S. Michalski
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  3. K. A. Kaufman
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  4. J. S. Ribeiro
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Michalski, R.S., Kerschberg, L., Kaufman, K.A. et al. Mining for knowledge in databases: The INLEN architecture, initial implementation and first results. J Intell Inf Syst 1, 85–113 (1992). https://doi.org/10.1007/BF01006415

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