Discovering regularities from knowledge bases
โ 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
No coin nor oath required. For personal study only.
โฆ 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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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