[Objective] This paper proposes a knowledge fusion method based on fuzzy ontologies, aiming to address the issues of representing and storing uncertain or inaccurate information in large-group emergency decision-making. [Methods] First, we used the multi-granular hesitant fuzzy language to construct fuzzy ontologies. Then, we implemented expert clustering based on K-Means and defined the value measure to determine cluster weights and realize knowledge fusion. Finally, we built an emergency knowledge base for the large group to find the optimal solutions. [Results] The proposed method could represent and store expert knowledge and utilize them in the emergency decision-making of a large group. The case analysis shows that our new method constructed an emergency knowledge base, improved the efficiency of knowledge fusion, and handled multi-stage emergency decision-making. [Limitations] The proposed model did not consider complex relationships among experts and only included the similarity of opinions in expert clustering. The attribute information can also be determined from other dimensions. [Conclusions] This study enriches the method of decision knowledge fusion and provides new directions for multi-stage emergency decision-making of large groups.
徐选华, 代笑含, 陈晓红. 大群体应急决策中基于价值测度的模糊本体知识融合方法及应用*[J]. 数据分析与知识发现, 2023, 7(4): 129-144.
Xu Xuanhua, Dai Xiaohan, Chen Xiaohong. Knowledge Fusion Method and Application for Fuzzy Ontologies Based on Value Measure in Large Group Emergency Decision-Making. Data Analysis and Knowledge Discovery, 2023, 7(4): 129-144.
(Chen Xiaohong, Zhang Weiwei, Xu Xuanhua. Large Group Decision-Making Method Based on Hesitation and Consistency under Social Network Context[J]. Systems Engineering- Theory & Practice, 2020, 40(5): 1178-1192.)
doi: 10.12011/1000-6788-2018-1559-15
(Wang Zhiying, Li Yongjian. Propagation Law and Coping Strategies for Public Opinions in Emergency with the Consideration of the Government Intervention[J]. Journal of Management Sciences in China, 2017, 20(2): 43-52, 62.)
[3]
Wu P, Wu Q, Zhou L G, et al. Optimal Group Selection Model for Large-Scale Group Decision Making[J]. Information Fusion, 2020, 61: 1-12.
doi: 10.1016/j.inffus.2020.03.002
(Jiang Xun, Su Xinning, Zhou Xin. An Exploratory Research on the Construction of Knowledge Base Collaborative Structure of Emergency Response Based on Adaptation Scenario Evolution[J]. Library and Information Service, 2017, 61(15): 60-71.)
doi: 10.13266/j.issn.0252-3116.2017.15.007
(Li Xiaonan, Liu Jin, Song Yafei. Combining Interval-Valued Belief Functions in the Framework of Intuitionistic Fuzzy Sets[J]. Systems Engineering-Theory & Practice, 2019, 39(11): 2906-2917.)
doi: 10.12011/1000-6788-2018-0522-12
(Xu Baoxiang, Ye Peihua. Research on the Method of Knowledge Representation[J]. Information Science, 2007, 25(5): 690-694.)
[7]
Dong X L, Gabrilovich E, Heitz G, et al. From Data Fusion to Knowledge Fusion[J]. Proceedings of the VLDB Endowment, 2014, 7(10): 881-892.
doi: 10.14778/2732951.2732962
(Zhang Lei, Wang Yanzhang. A Knowledge Fusion Method Considering the Fuzziness of Knowledge for Emergency Decision[J]. Systems Engineering-Theory & Practice, 2017, 37(12): 3235-3243.)
doi: 10.12011/1000-6788(2017)12-3235-09
[9]
Ji Y M, Liu K H, Liu S D, et al. FEPF: A Knowledge Fusion and Evaluation Method Based on PageRank and Feature Selection[C]// Proceedings of 2020 IEEE International Conference on Knowledge Graph. 2020: 635-640.
[10]
Kou T T, Tseng S S, Lin Y T. Ontology-Based Knowledge Fusion Framework Using Graph Partitioning[C]// Proceedings of the 16th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. 2003: 11-20.
(Liu Xiaojuan, Li Guangjian, Hua Bolin. Knowledge Fusion: From the Conceptual Understanding to the System Construction[J]. Library and Information Service. 2016, 60(13): 13-19, 32.)
doi: 10.13266/j.issn.0252-3116.2016.13.002
(Lu Quan, Liu Ting, Zhang Liangtao, et al. Study on Knowledge Discovery Model Based on Fuzzy Ontology Fusion and Reasoning[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(4): 333-344.)
(Han Xiangyu, Li Yan, Liu Yong, et al. A Knowledge Organization Method in Aerospace Field Based on Ontology[J]. Aerospace Industry Management, 2018 (4): 66-69.)
[14]
Shoaip N, Rezk A, El-Sappagh S, et al. A Comprehensive Fuzzy Ontology-Based Decision Support System for Alzheimer’s Disease Diagnosis[J]. IEEE Access, 2021, 9: 31350-31372.
doi: 10.1109/Access.6287639
[15]
Morente-Molinera J A, Wang Y L, Gong Z W, et al. Reducing Criteria in Multicriteria Group Decision-Making Methods Using Hierarchical Clustering Methods and Fuzzy Ontologies[J]. IEEE Transactions on Fuzzy Systems, 2022, 30(6): 1585-1598.
doi: 10.1109/TFUZZ.2021.3062145
[16]
Morente-Molinera J A, Cabrerizo F J, Mezei J, et al. A Dynamic Group Decision Making Process for High Number of Alternatives Using Hesitant Fuzzy Ontologies and Sentiment Analysis[J]. Knowledge-Based Systems, 2020, 195: 105657.
doi: 10.1016/j.knosys.2020.105657
(Guo Sitan, Pan Guangzhen, Zhao Lihui, et al. Recommendation System Based on Fuzzy Ontology and Genetic Algorithm[J]. Computer Engineering and Design, 2019, 40(3): 834-838.)
(Xu Xuanhua, Du Zhijiao, Chen Xiaohong, et al. Conflict Large-Group Emergency Decision-Making Method while Protecting Minority Opinions[J]. Journal of Management Sciences in China, 2017, 20(11): 10-23.)
[19]
Liu X, Xu Y J, Montes R, et al. Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(1): 159-171.
doi: 10.1109/TFUZZ.2018.2876655
(Wu Peng, Wu Qun, Zhou Ligang, et al. Hesitant Fuzzy Linguistic TOPSIS Decision Making Method Based on Multi-objective Attribute Weight Optimization[J]. Operations Research and Management Science, 2021, 30(6): 42-47.)
doi: 10.12005/orms.2021.0178
(Guan Qingyun, Chen Xuelong, Wang Yanzhang. Distance Entropy Based Decision-Making Information Fusion Method[J]. Systems Engineering-Theory & Practice, 2015, 35(1): 216-227.)
doi: 10.12011/1000-6788(2015)1-216
[22]
Zhan Q S, Fu C, Xue M. Distance-Based Large-Scale Group Decision-Making Method with Group Influence[J]. International Journal of Fuzzy Systems, 2021, 23(2): 535-554.
doi: 10.1007/s40815-020-00993-9
[23]
Xu X H, Yin X P, Chen X H. A Large-Group Emergency Risk Decision Method Based on Data Mining of Public Attribute Preferences[J]. Knowledge-Based Systems, 2019, 163: 495-509.
doi: 10.1016/j.knosys.2018.09.010
(Xu Xuanhua, Yu Zixin. A Large Group Emergency Decision Making Method and Application Based on Attribute Mining of Public Behaviour Big Data in Social Network Environment[J]. Control and Decision, 2022, 37(1): 175-184.)
(Liu Jian, Chen Jian, Liao Wenhe, et al. Dynamic Decision Process Based on Discrepancy of Decision Makers’ Risk Preferences[J]. Journal of Management Sciences in China, 2016, 19(4): 1-15.)
[26]
Zhou S H, Ji X, Xu X H. A Hierarchical Selection Algorithm for Multiple Attributes Decision Making with Large-Scale Alternatives[J]. Information Sciences, 2020, 521: 195-208.
doi: 10.1016/j.ins.2020.02.030
[27]
Tan X, Zhu J J, Cabrerizo F J, et al. A Cyclic Dynamic Trust-Based Consensus Model for Large-Scale Group Decision Making with Probabilistic Linguistic Information[J]. Applied Soft Computing, 2021, 100: 106937.
doi: 10.1016/j.asoc.2020.106937
[28]
Xu Z S. Deviation Measures of Linguistic Preference Relations in Group Decision Making[J]. Omega, 2005, 33: 249-254.
doi: 10.1016/j.omega.2004.04.008
[29]
Rodriguez R M, Martinez L, Herrera F. Hesitant Fuzzy Linguistic Term Sets for Decision Making[J]. IEEE Transactions on Fuzzy Systems, 2012, 20(1): 109-119.
doi: 10.1109/TFUZZ.2011.2170076
(Yu Wenyu, Zhong Qiuyan, Zhang Zhen. Multi-granular Hesitant Fuzzy Linguistic Group Decision Making with Incomplete Weight Information[J]. Systems Engineering-Theory & Practice, 2018, 38(3): 777-785.)
doi: 10.12011/1000-6788(2018)03-0777-09
[31]
Wei C P, Ren Z L, Rodríguez R M. A Hesitant Fuzzy Linguistic TODIM Method Based on a Score Function[J]. International Journal of Computational Intelligence Systems, 2015, 8(4): 701-712.
doi: 10.1080/18756891.2015.1046329
[32]
Calegari S, Ciucci D. Fuzzy Ontology, Fuzzy Description Logics and Fuzzy-OWL[C]// Proceedings of the 7th International Workshop on Fuzzy Logic and Applications:Applications of Fuzzy Sets Theory. 2007: 118-126.
[33]
Yu S M, Du Z J, Wang J Q, et al. Trust and Behavior Analysis-Based Fusion Method for Heterogeneous Multiple Attribute Group Decision-Making[J]. Computers & Industrial Engineering, 2021, 152: 106992.
[34]
Morente-Molinera J A, Kou G, Pang C, et al. An Automatic Procedure to Create Fuzzy Ontologies from Users’ Opinions Using Sentiment Analysis Procedures and Multi-granular Fuzzy Linguistic Modelling Methods[J]. Information Sciences, 2019, 476: 222-238.
doi: 10.1016/j.ins.2018.10.022
[35]
Ren Z Y, Liao H C, Liu Y X. Generalized Z-Numbers with Hesitant Fuzzy Linguistic Information and Its Application to Medicine Selection for the Patients with Mild Symptoms of the COVID-19[J]. Computers & Industrial Engineering, 2020, 145: 106517.
doi: 10.1016/j.cie.2020.106517
[36]
Du Y W, Chen Q C, Sun Y L, et al. Knowledge Structure-Based Consensus-Reaching Method for Large-Scale Multiattribute Group Decision-Making[J]. Knowledge-Based Systems, 2021, 219: 106885.
doi: 10.1016/j.knosys.2021.106885