article

Extending soft sets with description logics

Authors:

Yuncheng Jiang,

Suqin TangAuthors Info & Claims

Computers & Mathematics with Applications, Volume 59, Issue 6

Pages 2087 - 2096

https://doi.org/10.1016/j.camwa.2009.12.014

Published: 01 March 2010 Publication History

Abstract

Molodtsov initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. Description Logics (DLs) are a family of knowledge representation languages which can be used to represent the terminological knowledge of an application domain in a structured and formally well-understood way. The current research progress and the existing problems of soft set theory are analyzed. In this paper we extend soft sets with DLs, i.e., present an extended soft set theory by using the concepts of DLs to act as the parameters of soft sets. We define some operations for the extended soft sets. Moreover, we prove that certain De Morgan's laws hold in the extended soft set theory with respect to these operations.

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Cited By

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cover image Computers & Mathematics with Applications

Computers & Mathematics with Applications Volume 59, Issue 6

March, 2010

244 pages

ISSN:0898-1221

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Copyright © Elsevier Ltd © 2009.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2010

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Cited By

Amini AFirouzkouhi NGholami AGupta ACheng CDavvaz B(2022)Soft hypergraph for modeling global interactions via social media networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117466203:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117466
Han BWu RGeng SNie X(2022)A novel algorithm for all normal parameter reductions of a soft set based on object weighting and integer partitionApplied Intelligence10.1007/s10489-021-02906-152:10(11873-11891)Online publication date: 1-Aug-2022
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Warrier SMathew TAlcantud J(2020)Fuzzy soft matrices on fuzzy soft multiset and its applications in optimization problemsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-19117738:2(2311-2322)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/JIFS-191177
Fatimah FRosadi DHakim RAlcantud J(2018)N-soft sets and their decision making algorithmsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2838-622:12(3829-3842)Online publication date: 1-Jun-2018
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Xie NLi ZZhang G(2017)An intuitionistic fuzzy soft set method for stochastic decision-making applying prospect theory and grey relational analysisJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-1601333:1(15-25)Online publication date: 1-Jan-2017
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Zhang LHuang YWu XSkibniewski M(2017)Risk-based estimate for operational safety in complex projects under uncertaintyApplied Soft Computing10.1016/j.asoc.2017.01.02054:C(108-120)Online publication date: 1-May-2017
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Han BLi YGeng S(2017)01 Linear programming methods for optimal normal and pseudo parameter reductions of soft setsApplied Soft Computing10.1016/j.asoc.2016.08.05254:C(467-484)Online publication date: 1-May-2017
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Li ZXie NGao N(2017)Rough approximations based on soft binary relations and knowledge basesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2077-221:4(839-852)Online publication date: 1-Feb-2017
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Akram MNawaz S(2016)Fuzzy soft graphs with applicationsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/IFS-16210730:6(3619-3632)Online publication date: 1-Jan-2016
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