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Rough Forgetting
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. School of Intelligent Systems and Engineering, Jinan University (Zhuhai Campus), Zhuhai, China.ORCID iD: 0000-0002-6000-6581
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. Institute of Informatics, University of Warsaw, Warsaw, Poland.
2020 (English)In: Rough Sets. IJCRS 2020, Cham: Springer, 2020, Vol. 12179, p. 3-18Conference paper, Published paper (Refereed)
Abstract [en]

Recent work in the area of Knowledge Representation and Reasoning has focused on modification and optimization of knowledge bases (KB) through the use of forgetting operators of the form forget(KB, (R) over bar), where (R) over bar is a set of relations in the language signature used to specify the KB. The result of this operation is a new KB where the relations in (R) over bar are removed from the KB in a principled manner resulting in a more efficient representation of the KB for different purposes. The forgetting operator is also reflected semantically in terms of the relation between the original models of the KB and the models for the revised KB after forgetting. In this paper, we first develop a rough reasoning framework where our KBs consist of rough formulas with a semantics based on a generalization of Kleene algebras. Using intuitions from the classical case, we then define a forgetting operator that can be applied to rough KBs removing rough relations. A constructive basis for generating a new KB as the result of applying the forgetting operator to a rough KB is specified using second-order quantifier elimination techniques. We show the application of this technique with some practical examples.

Place, publisher, year, edition, pages
Cham: Springer, 2020. Vol. 12179, p. 3-18
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12179
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-169096DOI: 10.1007/978-3-030-52705-1_1ISI: 000713415600001ISBN: 9783030527051 (print)ISBN: 9783030527044 (print)OAI: oai:DiVA.org:liu-169096DiVA, id: diva2:1465049
Conference
International Joint Conference on Rough Sets. IJCRS 2020, Havana, Cuba, June 29 – July 3, 2020
Note

ELLIIT Network Organization for Information and Communication Technology, Sweden; Swedish Foundation for Strategic Research SSFSwedish Foundation for Strategic Research; Jinan University (Zhuhai Campus) [2017/27/B/ST6/02018]; National Science Centre PolandNational Science Centre, Poland

Available from: 2020-09-08 Created: 2020-09-08 Last updated: 2021-11-23Bibliographically approved

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Szalas, Andrzej

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Citation style
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