Abstract Modern database technologies process large volumes of data to discover new knowledge. Some large databases make discovery computationally expensive. Additional knowledge, known as domain or background knowledge, hidden in the database can often guide and restrict the search for interesting knowledge. This paper discusses mechanisms by which domain knowledge can be used effectively in discovering knowledge from databases. In particular, we look at the use of domain knowledge to reduce the search as well as to optimize the hypotheses which represent the interesting knowledge to be discovered. Also, we discuss how to use domain knowledge to test the validity of the discovered knowledge. Although domain knowledge can be used to improve database searches, it should not block the discovery of unexpected knowledge. We provide some guidelines to use domain knowledge properly.
[1]
Gregory Piatetsky-Shapiro,et al.
Knowledge Discovery in Databases: An Overview
,
1992,
AI Mag..
[2]
Michael G. Walker,et al.
How Feasible Is Automated Discovery?
,
1987,
IEEE Expert.
[3]
John Hayward,et al.
Hormones and Human Breast Cancer
,
1970,
Recent Results in Cancer Research.
[4]
Robert L. Blum.
Induction of Causal Relationships From a Time-Oriented Clinical Database: An Overview of the RX Project
,
1982,
AAAI.
[5]
Bruce G. Buchanan,et al.
Dendral and Meta-Dendral: Their Applications Dimension
,
1978,
Artif. Intell..
[6]
Steve Tanner,et al.
AI Research and Application Development at Boeing's Huntsville Laboratories
,
1993,
AI Mag..
[7]
Mark H. Chignell,et al.
Intelligent databases
,
1990
.