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A novel fuzzy knowledge graph pairs approach in decision making.

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Title: A novel fuzzy knowledge graph pairs approach in decision making.
Authors: Long, Cu Kim1,2 (AUTHOR), Van Hai, Pham1 (AUTHOR), Tuan, Tran Manh3 (AUTHOR) tmtuan@tlu.edu.vn, Lan, Luong Thi Hong3 (AUTHOR), Chuan, Pham Minh4 (AUTHOR), Son, Le Hoang5,6 (AUTHOR)
Source: Multimedia Tools & Applications. Jul2022, Vol. 81 Issue 18, p26505-26534. 30p.
Subjects: Knowledge graphs, Decision making, Approximate reasoning, Fuzzy graphs, Approximation algorithms, Soft sets, Two-way analysis of variance, Fuzzy systems
Abstract: Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records' label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A novel fuzzy knowledge graph pairs approach in decision making.
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  Data: <searchLink fieldCode="AR" term="%22Long%2C+Cu+Kim%22">Long, Cu Kim</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Van+Hai%2C+Pham%22">Van Hai, Pham</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tuan%2C+Tran+Manh%22">Tuan, Tran Manh</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> tmtuan@tlu.edu.vn</i><br /><searchLink fieldCode="AR" term="%22Lan%2C+Luong+Thi+Hong%22">Lan, Luong Thi Hong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chuan%2C+Pham+Minh%22">Chuan, Pham Minh</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Son%2C+Le+Hoang%22">Son, Le Hoang</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Jul2022, Vol. 81 Issue 18, p26505-26534. 30p.
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  Data: <searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Approximate+reasoning%22">Approximate reasoning</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+graphs%22">Fuzzy graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+algorithms%22">Approximation algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Soft+sets%22">Soft sets</searchLink><br /><searchLink fieldCode="DE" term="%22Two-way+analysis+of+variance%22">Two-way analysis of variance</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+systems%22">Fuzzy systems</searchLink>
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  Data: Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records' label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-022-13067-9
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        Type: general
      – SubjectFull: Decision making
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      – SubjectFull: Approximate reasoning
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      – SubjectFull: Fuzzy graphs
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      – SubjectFull: Approximation algorithms
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      – SubjectFull: Soft sets
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      – SubjectFull: Two-way analysis of variance
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      – TitleFull: A novel fuzzy knowledge graph pairs approach in decision making.
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              Text: Jul2022
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