2012 ExperiencewithDiscoveringKnowle

• (Compton, 2012) ⇒ Paul Compton. (2012). “Experience with Discovering Knowledge by Acquiring It.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339658

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Experience+with+Discovering+Knowledge+by+Acquiring+It
• http://dl.acm.org/citation.cfm?id=2339530.2339658&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; knowledge base; rule-based model

Abstract

Machines and people have complementary skills in Knowledge Discovery. Automated techniques can process enormous amounts of data to find new relationships, but generally these are represented by fairly simple models. On the other hand people are endlessly inventive in creating models to explain data at hand, but have problems developing consistent overall models to explain all the data that might occur in a domain; and the larger the model, the more difficult it becomes to maintain consistency. Ripple-Down Rules is a technique that has been developed to allow people to make real-time updates to a model whenever they notice some data that the model does not yet explain, while at the same time maintaining consistency. This allows an entire knowledge base to be built while it is already in use by making updates. There are now 100s of Ripple-Down-Rule knowledge bases in use and this paper presents some observations from log files tracking how people build these systems, and also outlines some recent research on how such techniques can be used to add greater specificity to the simpler models developed by automated techniques.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 ExperiencewithDiscoveringKnowle	Paul Compton			Experience with Discovering Knowledge by Acquiring It				10.1145/2339530.2339658		2012