融合TWD与FRS的隐性知识转移研究

doi:10.3778/j.issn.1673-9418.2008065

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (1): 253-260.DOI: 10.3778/j.issn.1673-9418.2008065

• 理论与算法 • 上一篇

融合TWD与FRS的隐性知识转移研究

张建华¹, 李方方¹^,⁺(), 刘艺琳¹, 杨岚²

1.郑州大学管理工程学院,郑州 450001
2.郑州大学机械与动力工程学院,郑州 450001

收稿日期:2020-08-20 修回日期:2020-10-27 出版日期:2022-01-01 发布日期:2020-11-09
通讯作者: + E-mail: 904822596@qq.com
作者简介:张建华（1975—）,男,河北承德人,博士,教授,博士生导师,主要研究方向为知识创新、知识服务。
李方方（1994—）,女,河南周口人,硕士研究生,主要研究方向为隐性知识服务。
刘艺琳（1996—）,女,河南周口人,硕士研究生,主要研究方向为隐性知识服务。
杨岚（1983—）,女,河南禹州人,讲师,主要研究方向为知识管理、知识服务。
基金资助:
国家社会科学基金(19BTQ035)

Research on Tacit Knowledge Transfer Based on Fusion of Three-Way Decision and Fuzzy Rough Set

ZHANG Jianhua¹, LI Fangfang¹^,⁺(), LIU Yilin¹, YANG Lan²

1. School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
2. School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China

Received:2020-08-20 Revised:2020-10-27 Online:2022-01-01 Published:2020-11-09
About author:ZHANG Jianhua, born in 1975, Ph.D., professor, Ph.D. supervisor. His research interests include knowledge innovation and knowledge service.
LI Fangfang, born in 1994, M.S. candidate. Her research interest is tacit knowledge service.
LIU Yilin, born in 1996, M.S. candidate. Her research interest is tacit knowledge service.
YANG Lan, born in 1983, lecturer. Her research interests include knowledge management and knowledge service.
Supported by:
National Social Science Foundation of China(19BTQ035)

摘要/Abstract

摘要：

面对日益丰富的知识资源,低成本、高精度的知识转移机理能够支撑知识服务型组织为知识用户提供良好知识服务、实现知识资源有效配置及提高知识资源利用率。以隐性知识为研究对象,针对知识本身的特性,提出基于三支决策的隐性知识转移模型。该模型主要包含三部分：用户知识需求与既有知识资源的匹配;根据转移成本确定决策阈值$\left( \alpha,\beta \right)$;根据决策阈值确定隐性知识转移的划分区域“转移”“不转移”和“延迟转移”。实证结果表明,与既有方法相比,提出的基于三支决策的隐性知识转移模型通过对转移区域的细分,降低了因错误分类而产生的知识转移成本;同时,根据视图相似度确定决策区域,提高了知识转移的准确性。

关键词: 知识服务, 隐性知识, 知识转移, 三支决策

Abstract:

In the face of increasingly abundant knowledge resources, low-cost, high-precision knowledge transfer mechanisms can support knowledge service organizations to provide good knowledge services for knowledge users, realize effective allocation of knowledge resources, and improve the utilization of knowledge resources. This paper takes tacit knowledge as the research object, and proposes a model of tacit knowledge transfer based on three-way decision aiming at the characteristics of knowledge itself. The model mainly includes three parts: matching the user’s knowledge needs with existing knowledge resources; determining the decision threshold $\left( \alpha,\beta \right)$ based on the transfer cost; determining the region of “transfer” “non-transfer” and “delayed transfer” based on decision threshold. The empirical results show that, compared with the existing methods, the implicit knowledge transfer model based on three-way decision proposed in this paper reduces the cost of knowledge transfer due to misclassification by subdividing the transfer area. Meanwhile, the decision area is determined according to the similarity of views, improving the accuracy of knowledge transfer.

Key words: knowledge service, tacit knowledge, knowledge transfer, three-way decision

中图分类号:

F270
TP18

张建华, 李方方, 刘艺琳, 杨岚. 融合TWD与FRS的隐性知识转移研究[J]. 计算机科学与探索, 2022, 16(1): 253-260.

ZHANG Jianhua, LI Fangfang, LIU Yilin, YANG Lan. Research on Tacit Knowledge Transfer Based on Fusion of Three-Way Decision and Fuzzy Rough Set[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(1): 253-260.

图/表 8

表1 二支决策模型

Table 1 Two-way decision

决策区域	用户知识评价	知识转移决策
正域 $(P (X [x]) ≥ λ)$	满意	转移
负域 $(P (X [x]) < λ)$	不满意	不转移

表1 二支决策模型

Table 1 Two-way decision

决策区域	用户知识评价	知识转移决策
正域 $(P (X [x]) ≥ λ)$	满意	转移
负域 $(P (X [x]) < λ)$	不满意	不转移

表2 知识转移成本矩阵

Table 2 Knowledge transfer cost matrix

知识转移决策	用户满意
知识转移决策	满意	不满意
转移 $(P P)$	$λ PP$	$λ PN$
延迟转移 $(P B)$	$λ BP$	$λ BN$
不转移 $(P N)$	$λ NP$	$λ NN$

表2 知识转移成本矩阵

Table 2 Knowledge transfer cost matrix

知识转移决策	用户满意
知识转移决策	满意	不满意
转移 $(P P)$	$λ PP$	$λ PN$
延迟转移 $(P B)$	$λ BP$	$λ BN$
不转移 $(P N)$	$λ NP$	$λ NN$

表3 属性权重

Table 3 Attribute weight

属性方面	属性权重	属性方面	属性权重
$a 1$	0.061 7	$a 7$	0.175 2
$a 2$	0.057 2	$a 8$	0.025 0
$a 3$	0.178 8	$a 9$	0.002 3
$a 4$	0.133 0	$a 10$	0.065 9
$a 5$	0.069 0	$a 11$	0.077 6
$a 6$	0.134 6

表3 属性权重

Table 3 Attribute weight

属性方面	属性权重	属性方面	属性权重
$a 1$	0.061 7	$a 7$	0.175 2
$a 2$	0.057 2	$a 8$	0.025 0
$a 3$	0.178 8	$a 9$	0.002 3
$a 4$	0.133 0	$a 10$	0.065 9
$a 5$	0.069 0	$a 11$	0.077 6
$a 6$	0.134 6

表4 用户知识需求

Table 4 User knowledge requirements

用户	属性
用户	$a 1$	$a 2$	$a 3$	$a 4$	$a 5$	$a 6$	$a 7$	$a 8$	$a 9$	$a 10$	$a 11$
$C 1$	0.58	0.98	0.60	0.02	0.17	0.11	0.18	0.45	0.80	0.15	0.16
$C 2$	0.71	0.71	0.30	0.29	0.28	0.82	0.11	0.78	0.82	0.39	0.62
$C 3$	0.38	0.22	0.30	0.56	0.12	0.86	0.53	0.19	0.68	0.58	0.36
$C 4$	0.93	0.23	0.30	0.34	0.10	0.38	0.43	0.04	0.89	0.94	0.21
$C 5$	0.07	0.34	0.20	0.37	0.14	0.46	0.97	0.60	0.17	0.45	0.30
$C 6$	0.87	0.05	0.40	0.19	0.68	0.28	0.07	0.82	0.32	0.88	0.69
$C 7$	0.93	0.84	0.60	0.33	0.21	0.32	0.18	0.56	0.84	0.69	0.15
$C 8$	0.30	0.57	0.20	0.77	0.36	0.27	0.01	0.19	0.18	0.31	0.93
$C 9$	0.62	0.24	0.30	0.36	0.36	0.89	0.29	0.45	0.71	0.97	0.53
$C 10$	0.49	0.73	0.10	0.62	0.21	0.76	0.41	0.19	0.08	0.59	0.33
$C 11$	0.96	0.65	0.80	0.24	0.22	0.20	0.95	0.65	0.60	0.01	0.73

表4 用户知识需求

Table 4 User knowledge requirements

用户	属性
用户	$a 1$	$a 2$	$a 3$	$a 4$	$a 5$	$a 6$	$a 7$	$a 8$	$a 9$	$a 10$	$a 11$
$C 1$	0.58	0.98	0.60	0.02	0.17	0.11	0.18	0.45	0.80	0.15	0.16
$C 2$	0.71	0.71	0.30	0.29	0.28	0.82	0.11	0.78	0.82	0.39	0.62
$C 3$	0.38	0.22	0.30	0.56	0.12	0.86	0.53	0.19	0.68	0.58	0.36
$C 4$	0.93	0.23	0.30	0.34	0.10	0.38	0.43	0.04	0.89	0.94	0.21
$C 5$	0.07	0.34	0.20	0.37	0.14	0.46	0.97	0.60	0.17	0.45	0.30
$C 6$	0.87	0.05	0.40	0.19	0.68	0.28	0.07	0.82	0.32	0.88	0.69
$C 7$	0.93	0.84	0.60	0.33	0.21	0.32	0.18	0.56	0.84	0.69	0.15
$C 8$	0.30	0.57	0.20	0.77	0.36	0.27	0.01	0.19	0.18	0.31	0.93
$C 9$	0.62	0.24	0.30	0.36	0.36	0.89	0.29	0.45	0.71	0.97	0.53
$C 10$	0.49	0.73	0.10	0.62	0.21	0.76	0.41	0.19	0.08	0.59	0.33
$C 11$	0.96	0.65	0.80	0.24	0.22	0.20	0.95	0.65	0.60	0.01	0.73

表5 三支决策知识转移数量

Table 5 The number of three-way decision knowledge transfer

转移决策	用户
转移决策	$C 1$	$C 2$	$C 3$	$C 4$	$C 5$	$C 6$	$C 7$	$C 8$	$C 9$	$C 10$	$C 11$
转移	56	1	8	1	0	0	55	0	1	3	2
延迟转移	80	7	4	21	0	0	87	1	7	10	0
不转移	1 464	1 592	1 588	1 578	1 600	1 600	1 458	1 599	1 592	1 587	1 598

表5 三支决策知识转移数量

Table 5 The number of three-way decision knowledge transfer

转移决策	用户
转移决策	$C 1$	$C 2$	$C 3$	$C 4$	$C 5$	$C 6$	$C 7$	$C 8$	$C 9$	$C 10$	$C 11$
转移	56	1	8	1	0	0	55	0	1	3	2
延迟转移	80	7	4	21	0	0	87	1	7	10	0
不转移	1 464	1 592	1 588	1 578	1 600	1 600	1 458	1 599	1 592	1 587	1 598

表6 条件概率计算结果

Table 6 Conditional probability calculation results

用户	$P (X \| [x])$	用户	$P (X \| [x])$
$C 1$	0.6	$C 7$	0.5
$C 2$	0.6	$C 8$	0.5
$C 3$	0.3	$C 9$	0.4
$C 4$	0.5	$C 10$	0.3
$C 5$	0.3	$C 11$	0.1
$C 6$	0.3

表6 条件概率计算结果

Table 6 Conditional probability calculation results

用户	$P (X \| [x])$	用户	$P (X \| [x])$
$C 1$	0.6	$C 7$	0.5
$C 2$	0.6	$C 8$	0.5
$C 3$	0.3	$C 9$	0.4
$C 4$	0.5	$C 10$	0.3
$C 5$	0.3	$C 11$	0.1
$C 6$	0.3

表7 知识转移成本

Table 7 Knowledge transfer costs

用户	$M$	用户	$M$
$C 1$	297.28	$C 7$	256.95
$C 2$	288.50	$C 8$	240.10
$C 3$	147.04	$C 9$	193.20
$C 4$	242.25	$C 10$	146.67
$C 5$	144.00	$C 11$	48.86
$C 6$	144.00

表7 知识转移成本

Table 7 Knowledge transfer costs

用户	$M$	用户	$M$
$C 1$	297.28	$C 7$	256.95
$C 2$	288.50	$C 8$	240.10
$C 3$	147.04	$C 9$	193.20
$C 4$	242.25	$C 10$	146.67
$C 5$	144.00	$C 11$	48.86
$C 6$	144.00

图1 知识转移成本比较

Fig.1 Comparison of knowledge transfer costs

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融合TWD与FRS的隐性知识转移研究

Research on Tacit Knowledge Transfer Based on Fusion of Three-Way Decision and Fuzzy Rough Set

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