Identifying Competitive Intelligence Based on Knowledge Element
Sun Lin1(), Wang Yanzhang2
1Economics and Management College, Dalian University, Dalian 116622, China 2Faculty of Management and Economics, Dalian University of Technology, Dalian 116023, China
[Objective] This study tries to identify competitive intelligence based on implicit correlated knowledge, aiming to help enterprises have upper hands in the fierce competition. [Methods] First, we constructed a knowledge system for competitive intelligence based on the metadata. Then we generated a network with the help of relationship among the attributes of these metadata. Finally we identifed competitive intelligencey through similarity analysis and merging multi-attributes. [Results] We successfully established a network for the properties of knowledge metadata from the enterprise’s financial and sales index, R&D ability and other resources. We identified the business ties based on the intelligence metadata of product HS, and merged the metadata of MGIS market planning. [Limitations] The proposed system could be improved with larger sample size. [Conclusions] This study solves the issues facing complex relation identification and intelligence analysis demands. It also benefits the competitive advantage evaluation, crisis warning, and decision making.
孙琳, 王延章. 基于知识元的企业竞争情报关系辨识与融合方法*[J]. 数据分析与知识发现, 2018, 2(6): 25-36.
Sun Lin,Wang Yanzhang. Identifying Competitive Intelligence Based on Knowledge Element. Data Analysis and Knowledge Discovery, 2018, 2(6): 25-36.
Artem P, Chun O, Alistair B, et al.Process Querying: Enabling Business Intelligence Through Query-Based Process Analytics[J]. Decision Support Systems, 2017, 100: 41-56.
doi: 10.1016/j.dss.2017.04.011
(Huang Xiaobin, Zhong Huixin.On the Innovation and Development of Enterprises Competitive Intelligence Analysis in the Big-data Era[J]. Library and Information, 2012(6): 9-14.)
doi: 10.3969/j.issn.1003-6938.2012.06.002
(Hua Bolin, Li Guangjian.Multi-source Fusion Competitive Intelligence in the Big Data Environment[J]. Information Studies: Theory & Application, 2015, 38(4): 1-5.)
[4]
Jason J J.Computational Collective Intelligence with Big Data: Challenges and Opportunities[J]. Future Generation Computer Systems, 2017, 66(1): 87-88.
doi: 10.1016/j.future.2016.08.021
[5]
赵洁. 面向WEB的企业竞争情报获取研究[D]. 合肥: 中国科学技术大学, 2013.
[5]
(Zhao Jie.Research on the Acquirement of Enterprise Competitive Intelligence in the Web[D]. Hefei: University of Science and Technology of China, 2013.)
(Sun Chunhua, Liu Yezheng.A Method for Combining Online Reviews’ Sentiment Orientation Based on Recognition of Relationship Between Product Feature Words[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(8): 844-852.)
doi: 10.3772/j.issn.1000-0135.2013.08.007
(Zhang Yufeng, He Chao, Li Lin.Research on Multi-dimensional Semantic Analysis of Dynamic Competitive Intelligence Based on On-line Analytical Mining[J]. Journal of the China Society for Scientific and Technical Information, 2012, 31(2): 166-173.)
doi: 10.3772/j.issn.1000-0135.2012.02.007
[8]
付慧蕾. 大数据环境下实体关系挖掘关键技术研究[D]. 北京: 北京交通大学, 2015.
[8]
(Fu Huilei.Key Technology Research on Entity Relation Mining in Big Data Environment[D]. Beijing: Beijing Jiaotong University, 2015.)
(Zhao Jie.A Framework of Acquirement and Fusion of Enterprise Competitive Intelligence Based on Relationship Extraction[J]. Journal of the China Society for Scientific and Technical Information, 2010, 29(2): 377-384.)
doi: 10.3772/j.issn.1000-0135.2010.02.025
[10]
姚衡. 基于贝叶斯网络的大数据因果关系挖掘[D]. 昆明:云南财经大学, 2016.
[10]
(Yao Heng.Causality Mining in Big Data Environments Based on Bayesian Network[D]. Kunming: Yunnan University of Finance and Economics, 2016.)
(Wang Yanzhang.Knowledge and Representation of Model Management[J]. Journal of System Engineering, 2011, 26(6): 850-856. )
[12]
Hermann B.The Unique Predication of Knowledge Element and Their Visualization and Factorization in Ontology Engineering[J]. Frontiers in Artificial Intelligence and Application, 2014, 267: 241-250.
doi: 10.3233/978-1-61499-438-1-251
[13]
孙琳, 王延章. Identifying the Core Competitive Intelligence Based on Enterprise Strategic Factors[J]. 上海交通大学学报: 英文版, 2015,20(1): 118-123.
[13]
(Sun L, Wang Y Z.Identifying the Core Competitive Intelligence Based on Enterprise Strategic Factors[J]. Journal of Shanghai Jiaotong University: Science, 2015, 20(1): 118-123.)
(Jiang Yurong.Challenge and Opportunity of Information in the Big Data Era[J]. Modern Information, 2013, 33(8): 58-60.)
doi: 10.3969/j.issn.1008-0821.2013.08.012
(Sun Lin, Wang Yanzhang.Knowledge Element Construction and Fusion Mechanism of Competitive Intelligence Based on Enterprise Resources[J]. Information Studies: Theory & Application, 2017, 40(7): 67-73.)
doi: 10.16353/j.cnki.1000-7490.2017.07.013
(Xiao Wenhui.Knowledge Unit Acquisition and Knowledge Unit Network Model of Unconventional Emergency[D]. Dalian: Dalian University of Technology, 2013.)
[18]
Dempster A.Upper and Lower Probabilities Induced by Multivalued Mapping[J]. Annals of Mathematical Statistics, 1967, 38(4): 325-339.
doi: 10.1214/aoms/1177698950
[19]
Shafer G.A Mathematical Theory of Evidence[M]. Princeton University Press, 1976.
[20]
Sun L, Wang Y Z.A Multi-attribute Fusion Approach Extending Dempster-Shafer Theory for Combinatorial-type Evidences[J]. Expert Systems with Applications, 2018, 96(4): 218-229.
doi: 10.1016/j.eswa.2017.12.005
[21]
Tversky A.Features of Similarity[J]. Psychological Review, 1977,84(4): 327-352.
doi: 10.1037/0033-295X.84.4.327
[22]
肖君德. 知识元相似度模型及融合方法研究[D]. 大连: 大连理工大学, 2012.
[22]
(Xiao Junde.Research on Similarity Model and Knowledge Fusion Method for Knowledge Element[D]. Dalian: Dalian University of Technology, 2012.)
[23]
Canedo V B, Canedo N S, Betanzos A A.Recent Advances and Emerging Challenges of Feature Selection in the Context of Big Data[J]. Knowledge-based System, 2015, 86: 33-45.
doi: 10.1016/j.knosys.2015.05.014
