๐”– Bobbio Scriptorium
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Discovering regularities from knowledge bases

โœ Scribed by Wei-Min Shen


Book ID
102867726
Publisher
John Wiley and Sons
Year
1992
Tongue
English
Weight
773 KB
Volume
7
Category
Article
ISSN
0884-8173

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โœฆ Synopsis


Knowledge bases open new horizons for machine learning research. One challenge is to design learning programs to expand the knowledge base using the knowledge that is currently available. This article addresses the problem of discovering regularities in large knowledge bases that contain many assertions in different domains. The article begins with a definition of regularities and gives the motivation for such a definition. It then outlines a framework that attempts to integrate induction with knowledge. Although the implementation of the framework currently uses only a statistical method for confirming hypotheses, its application to a real knowledge base has shown some encouraging and interesting results.


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Discovering knowledge from noisy databas
โœ Wong, Man Leung ;Leung, Kwong Sak ;Cheng, Jack C. Y. ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 169 KB

In data mining, we emphasize the need for learning from huge, incomplete, and imperfect data sets. To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting