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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16408

Title: Quantitatively evaluating formula-variable relevance by forgetting
Authors: LIANG, Xin
Lin, Zuoquan
Issue Date: 2013
Publisher: Springer Berlin Heidelberg
Citation: R. Zaïane, Osmar; Zilles, Sandra (Ed.). Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings, p. 271-277
Series/Report: Lecture Notes in Computer Science
Series/Report no.: 7884
Abstract: Forgetting is a feasible tool for weakening knowledge bases by focusing on the most important issues, and ignoring irrelevant, outdated, or even inconsistent information, in order to improve the efficiency of inference, as well as resolve conflicts in the knowledge base. Also, forgetting has connections with relevance between a variable and a formula. However, in the existing literature, the definition of relevance is “binary” – there are only the concepts of “relevant” and “irrelevant”, and no means to evaluate the “degree” of relevance between variables and formulas. This paper presents a method to define the formula-variable relevance in a quantitative way, using the tool of variable forgetting, by evaluating the change of model set of a certain formula after forgetting a certain variable in it. We also discuss properties, examples and one possible application of the definition.
URI: http://hdl.handle.net/1942/16408
Link to publication: http://alpha.uhasselt.be/~lucp1080/xin.pdf
DOI: 10.1007/978-3-642-38457-8_26
ISBN: 978-3-642-38457-8
ISSN: 0302-9743
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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