Cited By
View all- Düggelin WLaurenzi E(2024)A Knowledge Graph-Based Decision Support System for Resilient Supply Chain NetworksResearch Challenges in Information Science10.1007/978-3-031-59465-6_5(66-81)Online publication date: 2-May-2024
Research on Search Method of Knowledge Graph of Supply Chain Risk Based on Cascading Effect
Authors: 
Qiushi Zhang, Wenxin MuAuthors Info & Claims
Qiushi Zhang, Wenxin MuAuthors Info & ClaimsPages 111 - 119
Abstract
Each enterprise in the supply chain is more susceptible to risks while obtaining more benefits through collaboration. In order to achieve the purpose of reducing cost and increasing efficiency, enterprises should fully understand the risk and choose the best control point. The risk knowledge graph based on DRC chain stores the risk chain composed of multiple risk sources, risks and consequences. In order to fully understand the risk, this study uses the above knowledge graph to design the corresponding graph search method according to the dynamic characteristics of the risk queue of temporary risk. The experimental results show that the search method in this study can match the nodes well, and can search the structure subgraph related to the matching nodes in the knowledge graph to evaluate the system risk, and provide support for selecting the best control point.
References
[1]
J.Li, ‘Cascading Effects of Risk dedicated to Collaborative Networks’, Doctoral Dissertation, Beijing Jiaotong University. (in Chinese)
[2]
J. Li, F. Bénaben, J. Gou, and W. Mu, ‘A Proposal for Risk Identification Approach in Collaborative Networks Considering Susceptibility to Dange’, in Collaborative Networks of Cognitive Systems, vol. 534, L. M. Camarinha-Matos, H. Afsarmanesh, and Y. Rezgui, Eds. Cham: Springer International Publishing, 2018, pp. 74–84.
[3]
Q. Xu, X. Wang, J. Li, Y. Gan, L. Chai, and J. Wang, ‘StarMR: An Efficient Star-Decomposition Based Query Processor for SPARQL Basic Graph Patterns Using MapReduce’, in Web and Big Data, vol. 10987, Y. Cai, Y. Ishikawa, and J. Xu, Eds. Cham: Springer International Publishing, 2018, pp. 415–430.
[4]
G. Liu, Q. Shi, K. Zheng, A. Liu, Z. Li, and X. Zhou, ‘An efficient method for top-k graph based node matching’, World Wide Web, vol. 22, no. 3, pp. 945–966, May 2019.
[5]
A. Shemshadi, Q. Z. Sheng, and Y. Qin, ‘Efficient pattern matching for graphs with multi-Labeled nodes’, Knowledge-Based Systems, vol. 109, pp. 256–265, Oct. 2016.
[6]
A. Habi, B. Effantin, and H. Kheddouci, ‘Diversified top-k search with relaxed graph simulation’, Soc. Netw. Anal. Min., vol. 9, no. 1, p. 55, Dec. 2019.
[7]
W. Wang, Y. Yao, L. Zhu, X. Hei, and Y. Wang, ‘A Novel Subgraph Querying Method on Directed Weighted Graphs’, in 2018 14th International Conference on Computational Intelligence and Security (CIS), Hangzhou, China, Nov. 2018, pp. 150–154.
[8]
J. Jin, J. Luo, S. Khemmarat, and L. Gao, ‘Querying Web-Scale Knowledge Graphs Through Effective Pruning of Search Space’, IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 8, pp. 2342–2356, Aug. 2017.
[9]
S. Yang, F. Han, Y. Wu, and X. Yan, ‘Fast top-k search in knowledge graphs’, in 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, May 2016, pp. 990–1001.
[10]
G. Li, L. Yan, and Z. Ma, ‘An approach for approximate subgraph matching in fuzzy RDF graph’, Fuzzy Sets and Systems, vol. 376, pp. 106–126, Dec. 2019.
[11]
S. Hong, N. Park, T. Chakraborty, H. Kang, and S. Kwon, ‘PAGE: Answering Graph Pattern Queries via Knowledge Graph Embedding’, in Big Data – BigData 2018, vol. 10968, F. Y. L. Chin, C. L. P. Chen, L. Khan, K. Lee, and L.-J. Zhang, Eds. Cham: Springer International Publishing, 2018, pp. 87–99.
[12]
G. Sun, G. Liu, Y. Wang, M. A. Orgun, and X. Zhou, ‘Incremental Graph Pattern Based Node Matching’, in 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, Apr. 2018, pp. 281–292.
[13]
Q. Miao and Z. T. Xia, ‘Design and Application of Risk Early-Warning System of Electric Power Engineering Projects - A Case Study of Combined Heat and Power Project in Western China’, AMM, vol. 278–280, pp. 2113–2117, Jan. 2013.
[14]
E. Wang, W. Wei, B. D. Wang, and Z. Liu, ‘Real-Time Dispatching Operation Risk Assessment Based on Fault Tree Theory’, AMR, vol. 732–733, pp. 909–914, Aug. 2013.
[15]
P. Jiang, X. Liu, J. Zhang, S. H. Te, and K. Y. H. Gin, ‘Latent Variable Structured Bayesian Network for Cyanobacterial Risk Pre-control’, in 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bangkok, Dec. 2018, pp. 829–833.
[16]
M.-J. Chang, ‘A Support Vector Machine Forecasting Model for Typhoon Flood Inundation Mapping and Early Flood Warning Systems’, Water, vol. 10, no. 12, p. 1734, Nov. 2018.
[17]
H. Liang, G. Li, and W. Liang, ‘Intelligent early warning model of early-stage overflow based on dynamic clustering’, Cluster Comput, vol. 22, no. S1, pp. 481–492, Jan. 2019.
[18]
A. Krzemień, ‘Dynamic fire risk prevention strategy in underground coal gasification processes by means of artificial neural networks’, Mar. 2019.
[19]
H. Liang, J. Zou, M. Junaid Khan, and H. Jinxuan, ‘An Sand Plug of Fracturing Intelligent Early Warning Model Embedded in Remote Monitoring System’, IEEE Access, vol. 7, pp. 47944–47954, 2019.
[20]
C. Yan, L. Wang, W. Liu, and M. Qi, ‘Financial early warning of non-life insurance company based on RBF neural network optimized by genetic algorithm’, Concurrency Computat Pract Exper, vol. 30, no. 23, p. e4343, Dec. 2018.
[21]
Y. Peng, Y. Zhang, and Y. Wei, ‘Intelligent early-warning analysis of operational risks based on grey Kalman filter’, IFS, vol. 37, no. 5, pp. 5801–5808, Nov. 2019.
Index Terms
- Research on Search Method of Knowledge Graph of Supply Chain Risk Based on Cascading Effect
Index terms have been assigned to the content through auto-classification.
Recommendations
Risk matrix driven supply chain risk management: Adapting risk matrix based tools to modelling interdependent risks and risk appetite
Highlights- A major research gap is identified in the literature on supply chain risk management.
AbstractThere is a major research gap of developing a supply chain risk management process integrating the risk appetite of a decision maker and all stages of the risk management process within an interdependent network of systemic risks. We ...
Supply Chain Risk Assessment Based on AHP and Fuzzy Comprehensive Evaluation
ICMECG '10: Proceedings of the 2010 International Conference on Management of e-Commerce and e-GovernmentSupply chain risk management is a newly-emerged key subject of supply chain management area. This paper classified and refined supply chain risk according to supply chain risk sources, and established a risk assessment index system. Meanwhile by using ...
Novel Method for Fishery Supply Chain Risk Diversity and Insurance
ICEE '10: Proceedings of the 2010 International Conference on E-Business and E-GovernmentSupply chain management had become part of the senior management agenda in the manufacturing and retailing industries. More recently, interest in supply chain management has also been growing in the fishery industry both in developed and developing ...
Comments
Information & Contributors
Information
Published In
April 2021
227 pages
ISBN:9781450388887
DOI:10.1145/3460824
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 July 2021
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- the National Science Foundation for Young Scientists of China
Conference
MSIE 2021
MSIE 2021: 2021 3rd International Conference on Management Science and Industrial Engineering
April 2 - 4, 2021
Osaka, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 106Total Downloads
- Downloads (Last 12 months)20
- Downloads (Last 6 weeks)1
Reflects downloads up to 12 Aug 2024
Other Metrics
Citations
Cited By
View all- Düggelin WLaurenzi E(2024)A Knowledge Graph-Based Decision Support System for Resilient Supply Chain NetworksResearch Challenges in Information Science10.1007/978-3-031-59465-6_5(66-81)Online publication date: 2-May-2024
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format