Knowledge Fusion Method and Application for Fuzzy Ontologies Based on Value Measure in Large Group Emergency Decision-Making

doi:10.11925/infotech.2096-3467.2022.0347

Data Analysis and Knowledge Discovery

2023, Vol. 7

Issue (4): 129-144 DOI: 10.11925/infotech.2096-3467.2022.0347

Current Issue | Archive | Adv Search

Knowledge Fusion Method and Application for Fuzzy Ontologies Based on Value Measure in Large Group Emergency Decision-Making

Xu Xuanhua,Dai Xiaohan(

),Chen Xiaohong

Business School, Central South University, Changsha 410083, China

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Abstract

[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.

Key words： Large Group Emergency Decision-Making Value Measurement Fuzzy Ontologies Knowledge Fusion

Received: 14 April 2022 Published: 07 June 2023

ZTFLH:

TP273 C934

Fund:National Natural Science Foundation of China(71971217);National Natural Science Foundation of China(72091515);National Natural Science Foundation of China(71790615)

Corresponding Authors: Dai Xiaohan，ORCID：0000-0001-8821-3761，E-mail：dxh2714980900@163.com

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Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0347 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I4/129

Fuzzy Ontology Scheme

The Framework of Fuzzy Ontologies Knowledge Fusion

*	$c 1$	$c 2$	$c 3$
$x 1$	0.103 5	1	0.750 0
$x 2$	0.333 3	0.575 4	1
$x 3$	1	0.103 5	0.203 6
$x 4$	0.833 3	0	0.075 4

The Relationship Values of

e i

e i
">

The Graphical Representation of Fuzzy Ontology for $e i$

个体	$c 1$	$c 2$	$c 3$	$c 4$	$c 5$
$x 1$	0.2	0.8	0.6	0.3	0.1
$x 2$	0.3	0	0.3	0.1	0.6
$x 3$	0	1	0.2	0.1	0.7

The Relationship Values Between Alternatives and Attributes

The Hierarchical Clustering

聚集	决策者
$C 1$	$e 1, e 4, e 5, e 9, e 15, e 25, e 26$
$C 2$	$e 2, e 3$
$C 3$	$e 6, e 11, e 12$
$C 4$	$e 7, e 14, e 19$
$C 5$	$e 8, e 17, e 23, e 24, e 27, e 29, e 30$
$C 6$	$e 10, e 13, e 22, e 28$
$C 7$	$e 16$
$C 8$	$e 18, e 20, e 21$

The Clustering Result of Experts

指标	$C 1$	$C 2$	$C 3$	$C 4$	$C 5$	$C 6$	$C 7$	$C 8$
$η k$	0.240	0.036	0.089	0.106	0.236	0.145	0.025	0.122
$K M k$	0.115	0.100	0.108	0.106	0.122	0.080	0.129	0.112
$w k$	0.275	0.036	0.095	0.112	0.286	0.030	0.030	0.136

Weights of Clusters

方案	$c 1$	$c 2$	$c 3$	$c 4$	$c 5$
$x 1$	0.866 1	0.539 7	0.552 5	0.330 1	0.220 9
$x 2$	0.542 1	0.526 9	0.515 4	0.359 9	0.314 6
$x 3$	0.727 7	0.607 3	0.693 9	0.365 0	0.243 7
$x 4$	0.425 2	0.552 7	0.439 8	0.554 0	0.401 1
$x 5$	0.298 9	0.419 2	0.517 1	0.727 7	0.671 7
$x 6$	0.258 2	0.353 3	0.381 5	0.891 2	0.807 5

The Relationship Value Between

x l

and

c j

Graphic Structure of Group Fuzzy Ontology

属性（ $c j$ ）	关键词（h）	权重（ $w j$ ）
人民生命健康安全（ $c 1$ ）	冠状病毒、肺炎、感染、病人、勤洗手、生命安全、就医、健康、 $?$	0.547
社会影响度（ $c 2$ ）	报道、可怕、不信谣、人心惶惶、宣传、 $?$	0.142
中国疫情防控和物资需求（ $c 3$ ）	隔离、口罩、春运、防护、发热、春节假期、医疗、支援、聚集、 $?$	0.305
经济发展（ $c 4$ ）	经济、工作、…	0.000
全球疫情防控（ $c 5$ ）	武汉、广东、韩国、泰国、日本、国家、 $?$	0.007

Evaluation Attributes and Weights of

t 1

阶段	w_j
阶段	$c 1$	$c 2$	$c 3$	$c 4$	$c 5$
$t 2$	0.336 0	0.311 0	0.338 0	0.001 0	0.014 0
$t 3$	0.203 0	0.186 0	0.293 0	0.112 0	0.206 0

Attribute Weights of

{t 2, t 3}

阶段	$s v x l$
阶段	$x 1$	$x 2$	$x 3$	$x 4$	$x 5$	$x 6$	$x *$
$t 2$	0.649 0	0.525 0	0.671 7	0.469 6	0.415 7	0.337 8	$x 3$
$t 3$	/	/	/	0.462 7	0.510 0	0.496 1	$x 5$

Alternative Information in

{t 2, ? t 3}

方案	$c 1$	$c 2$	$c 3$	$c 4$	$c 5$	$s v x l$
$x 1$	0.866 1	0.539 7	0.552 5	0.330 1	0.220 9	0.477
$x 2$	0.542 1	0.526 9	0.515 4	0.359 9	0.314 6	0.432
$x 3$	0.727 7	0.607 3	0.693 9	0.365 0	0.243 7	0.497
$x 4$	0.425 2	0.552 7	0.439 8	0.554 0	0.401 1	0.470
$x 5$	0.298 9	0.419 2	0.517 1	0.727 7	0.671 7	0.557
$x 6$	0.258 2	0.353 3	0.381 5	0.891 2	0.807 5	0.593
$x 7$	0.325 9	0.298 1	0.331 6	0.877 4	0.911 7	0.612

The Updated Fuzzy Ontology

方法	方案	$c 1$	$c 2$	$c 3$	$c 4$	$c 5$
本文方法	$x 1$	0.866 1	0.539 7	0.552 5	0.330 1	0.220 9
	$x 2$	0.542 1	0.526 9	0.515 4	0.359 9	0.314 6
	$x 3$	0.727 7	0.607 3	0.693 9	0.365 0	0.243 7
	$x 4$	0.425 2	0.552 7	0.439 8	0.554 0	0.401 1
	$x 5$	0.298 9	0.419 2	0.517 1	0.727 7	0.671 7
	$x 6$	0.258 2	0.353 3	0.381 5	0.891 2	0.807 5
文献[8] 方法	$x 1$	0.932 7	0.364 5	0.224 0	0.560 9	0.163 9
	$x 2$	0.826 4	0.504 5	0.718 6	0.061 2	0.080 7
	$x 3$	0.339 9	0.634 3	0.647 7	0.204 1	0.344 4
	$x 4$	0.329 0	0.316 0	0.183 1	0.366 7	0.164 0
	$x 5$	0.227 8	0.566 6	0.758 5	0.286 3	0.757 8
	$x 6$	0.156 9	0.524 9	0.525 9	0.889 0	0.729 2
文献[21] 方法	$x 1$	0.799 8	0.541 8	0.567 8	0.414 0	0.437 8
	$x 2$	0.697 6	0.603 3	0.669 2	0.379 4	0.488 3
	$x 3$	0.532 3	0.523 2	0.462 4	0.359 1	0.304 4
	$x 4$	0.461 8	0.570 1	0.464 1	0.561 6	0.456 8
	$x 5$	0.342 5	0.448 5	0.539 3	0.621 8	0.454 2
	$x 6$	0.316 8	0.361 4	0.409 4	0.821 4	0.758 2

The Comparisons of Fusion Results

方法	$x 1$	$x 2$	$x 3$	$x 4$	$x 5$	$x 6$	方案排序
本文方法	0.720 0	0.531 0	0.698 0	0.448 0	0.385 0	0.313 0	$x 1 ? x 3 ? x 2 ? x 4 ? x 5 ? x 6$
文献[8] 方法	0.631 0	0.743 0	0.476 0	0.282 0	0.442 0	0.326 0	$x 2 ? x 1 ? x 3 ? x 5 ? x 6 ? x 4$
文献[21] 方法	0.691 0	0.675 0	0.509 0	0.478 0	0.419 0	0.355 0	$x 1 ? x 2 ? x 3 ? x 4 ? x 5 ? x 6$

The Comparison of Alternative Values in

t 1

方法	各阶段方案排序结果
方法	$t 1$	$t 2$
本文方法	$x 1 ? x 3 ? x 2 ? x 4 ? x 5 ? x 6$	$x 3 ? x 1 ? x 2 ? x 4 ? x 5 ? x 6$
未考虑价值测度的方法	$x 1 ? x 3 ? x 2 ? x 4 ? x 5 ? x 6$	$x 2 ? x 1 ? x 3 ? x 4 ? x 5 ? x 6$
文献[20]方法	$x 1 ? x 2 ? x 3 ? x 6 ? x 5 ? x 4$	/
文献[31]方法	$x 1 ? x 2 ? x 3 ? x 4 ? x 6 ? x 5$	$x 1 ? x 2 ? x 3 ? x 4 ? x 5 ? x 6$

The Comparisons of Methods in

t 1

and

t 2

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