Abstract
Purely technocentric decision-making approach cannot respond to digital challenges in a reliable way, due to the lack of human experience and collaborative involvement. Combining decision-making approach with human experience knowledge, context components and real-time data analysis, collaborative decision-making can be achieved more effectively. Accordingly, we construct a “context-event-knowledge” three-layer integrated knowledge model (hereinafter referred to as “integrated model”) to define the concepts and transformation rules of knowledge information required for decision-making. From the perspective of event evolution, it systematically depicts the decision-making logic in a real circumstance and presents in the form of a service architecture. Finally, the effectiveness of our method is verified through a practical case of railway freight scheduling. The main contributions of our research are as follows: (1) The integrated model for collaborative decision-making is proposed with a human experience knowledge centered angle; (2) Achieved a more responsible and resilient decision-making process, which improving the decision-making efficiency; (3) Through the integration of human experience and service-oriented architecture, we can provide solutions for collaborative decision-making in different contexts. It has certain practical guiding significance for relevant enterprises and departments to carry out process control and emergency management.
“Key Project of China National Railway Group Corporation’s Science and Technology Research and Development” –“Research on Key Technologies for Railway Operation and Maintenance Safety Risk Prevention and Control Based on Global Data Linkage” (N2021S017)
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References
Kim, C.N., Chung, H.M., Paradice, D.B.: Inductive modeling of expert decision making in loan evaluation: a decision strategy perspective. Decis. Support Syst. 21, 83–98 (1997). https://doi.org/10.1016/S0167-9236(97)00022-5
Januzzi, J.L., et al.: 2019 methodology for creating expert consensus decision pathways a report of the American College of Cardiology. J. Am. Coll. Cardiol. 74, 1138–1150 (2019). https://doi.org/10.1016/j.jacc.2019.05.035
Chen, G., Zeng, D., Wei, Q., Zhang, M., Guo, X.: Transitions of decision-making paradigms and enabled innovations in the context of big data. Manage. World. 36, 95–105+220 (2020). https://doi.org/10.19744/j.cnki.11-1235/f.2020.0023
Lu, J., Yan, Z., Han, J., Zhang, G.: Data-driven decision-making ((DM)-M-3): framework, methodology, and directions. IEEE Trans. Emerg. Top. Comput. Intell. 3, 286–296 (2019). https://doi.org/10.1109/TETCI.2019.2915813
Hua, B., Wei, M.: Research and practice of reasoning-assisted decision-making methods for injury crimes. Data Anal. Knowl. Discov. 1–21
Rajabi, E., Etminani, K.: Towards a knowledge graph-based explainable decision support system in healthcare. Stud. Health. Technol. Inform. 281, 502–503 (2021). https://doi.org/10.3233/SHTI210215
Xu, X., Zhu, Y.: Data-driven dynamic collaborative method of public and experts in large-group emergency decision-making. Syst. Eng. Electron. 1–15
Chen, X., Zhang, W., Xu, X., Cao, W.: A public and large-scale expert information fusion method and its application: mining public opinion via sentiment analysis and measuring public dynamic reliability. Inf. Fusion. 78, 71–85 (2022). https://doi.org/10.1016/j.inffus.2021.09.015
Drissen-Silva, M.V., Rabelo, R.J.: A model for collaborative decision-making for the evolution of virtual enterprises. In: Camarinha-Matos, L.M., Xu, L., Afsarmanesh, H. (eds.) PRO-VE 2012. IAICT, vol. 380, pp. 655–663. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32775-9_65
Li, Z., Ding, X., Liu, T.: Constructing narrative event evolutionary graph for script event prediction. In: Lang, J. (ed.) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 4201–4207. IJCAI- International Joint Conference on Artificial Intelligence, Freiburg (2018)
Zhang, W., Wang, J., Wang, Y., Sun, Z.: Constructing and inferring event logic cognitive graph in the field of big data. Sci. Sin. Inf. 50, 988–1002 (2020). https://doi.org/10.1360/SSI-2019-0273
Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Jointly embedding knowledge graphs and logical rules. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 192–202. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1019
Li, Z., Zhao, S., Ding, X., Liu, T.: EEG: knowledge base for event evolutionary principles and patterns. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds.) SMP 2017. CCIS, vol. 774, pp. 40–52. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6805-8_4
Yang, Y., Wei, Z., Chen, Q., Wu, L.: Using external knowledge for financial event prediction based on graph neural networks. In: Proceedings of the 28th ACM International Conference on Information & Knowledge Management (CIKM 2019), pp. 2161–2164. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3357384.3358156
Zhao, W., Wang, Y., Jiang, X., Hu, W.: Construction and interactive analysis tool for military event evolutionary graph. Command Inf. Syst. Technol. 13, 59–64 (2022). https://doi.org/10.15908/j.cnki.cist.2022.03.010
Wang, X., Wang, X., Li, Y.: Research on the construction of the event evolution graph of UGC of network public options for major emergencies—taking the natural disaster 7·20 torrential rain in Henan as an example. Libr. Inf. Serv. 66, 13–23 (2022). https://doi.org/10.13266/j.issn.0252-3116.2022.16.002
Shan, X., Pang, S., Liu, X., Yang, J.: Research on internet public opinion event prediction method based on event evolution graph. Inf. Stud. Theory Appl. 43, 165–170+156 (2020). https://doi.org/10.16353/j.cnki.1000-7490.2020.10.027
Hetemi, E., Pushkina, O., Zerjav, V.: Collaborative practices of knowledge work in IT projects. Int. J. Project Manage. 40, 906–920 (2022). https://doi.org/10.1016/j.ijproman.2022.10.004
Liu, Z., Zeng, X., Zhang, Z.: Research on construction and analysis of financial emergencies knowledge graph with event logical for emergency management. J. Inf. Res. Manage. 12, 137–151 (2022). https://doi.org/10.13365/j.jirm.2022.03.137
Xia, L., Chen, J., Yu, H.: Research on the visual summary generation of network public opinion events based on multi-dimensional characteristics of event evolution graph. Inf. Stud. Theory Appl. 43, 157–164 (2020). https://doi.org/10.16353/j.cnki.1000-7490.2020.10.026
Benaben, F., et al.: A tentative framework for risk and opportunity detection in a collaborative environment based on data interpretation. Presented at the Hawaii International Conference on System Sciences (2019). https://doi.org/10.24251/HICSS.2019.369
Benaben, F., Lauras, M., Truptil, S., Salatge, N.: A metamodel for knowledge management in crisis management. In: Bui, T.X., Sprague, R.H. (eds.) Proceedings of the 49th Annual Hawaii International Conference on System Sciences (HICSS 2016), pp. 126–135. IEEE Computer Society, Los Alamitos (2016). https://doi.org/10.1109/HICSS.2016.24
Li, P., Li, Y.: Event logic graph construction method for event profile. Command Inf. Syst. Technol. 12, 54–60+69 (2021). https://doi.org/10.15908/j.cnki.cist.2021.05.010
Chu, F.: Research on reliability and emergency disposal of high speed railway dispatch command system (2012). https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C475KOm_zrgu4lQARvep2SAkVR3-_UaYGQCi3Eil_xtLbzB-iGGAWfJ1HOLO17J6TVHb_RKOlmOgQsAL4nzBhayw&uniplatform=NZKPT
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Liu, X., Mu, W., Gou, J., Zhou, Q., Zhang, J. (2023). Integrated Modeling and Collaborative Decision-Making Method Based on Event Logic Knowledge Graph. In: Camarinha-Matos, L.M., Boucher, X., Ortiz, A. (eds) Collaborative Networks in Digitalization and Society 5.0. PRO-VE 2023. IFIP Advances in Information and Communication Technology, vol 688. Springer, Cham. https://doi.org/10.1007/978-3-031-42622-3_24
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DOI: https://doi.org/10.1007/978-3-031-42622-3_24
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