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Using domain knowledge to optimize the knowledge discovery process in databases
Author(s) -
Owrang O. M. Mehdi
Publication year - 2000
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(200001)15:1<45::aid-int3>3.0.co;2-h
Subject(s) - knowledge extraction , computer science , domain knowledge , domain (mathematical analysis) , process (computing) , database , business process discovery , knowledge based systems , software mining , data science , data mining , information retrieval , artificial intelligence , work in process , software , engineering , mathematical analysis , mathematics , programming language , software construction , operations management , business process modeling , software system , business process , operating system
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, 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 size of the database for discovery, to optimize the hypotheses which represent the interesting knowledge to be discovered, to optimize the queries used to prove the hypotheses, and to avoid possible redundant and contradictory rule discovery. Some experimental results using the IDIS knowledge discovery tool is provided. ©2000 John Wiley & Sons, Inc.