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Construction of the knowledge service model of a port supply chain enterprise in a big data environment

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Abstract

In the context of the rapid development of big data and artificial intelligence, knowledge service theory and big data technology are applied to build a smart port supply chain knowledge service model. This model provides a personalized, intelligent, and diversified knowledge-based service system platform solution to port supply chain enterprises, helping to realize port supply chain transformation and upgrading and intelligent integrated operations. This paper analyses and summarizes the research status on knowledge service demand and port supply chain knowledge service during the development and operation of the port supply chain and applies big data and artificial intelligence technologies such as knowledge matching, knowledge fusion, and natural language processing. A port supply chain knowledge service model including knowledge acquisition, knowledge organization and knowledge service modules is constructed. The ontology method is used to construct the ontology knowledge base of the port supply chain, and based on this, computational reasoning experiments are performed. The experiments show that ontology technology demonstrates effectiveness and superiority in constructing a knowledge service system model for the port supply chain in terms of knowledge representation and knowledge reasoning.

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Acknowledgements

This work was financially supported by Jiangxi University Humanities and Social Science Research Project (GL18103); Social Science Planning Project of Jiangxi Province (19TQ01); and Jiangxi Provincial Department of Education Science and Technology Research Key Project (GJJ180249).

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  1. School of Information Management, Jiangxi University of Finance and Economics, Nanchang, 330013, Jiangxi, China

    Yang Bo & Yang Meifang

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  1. Yang Bo
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  2. Yang Meifang
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Correspondence to Yang Meifang.

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Bo, Y., Meifang, Y. Construction of the knowledge service model of a port supply chain enterprise in a big data environment. Neural Comput & Applic 33, 1699–1710 (2021). https://doi.org/10.1007/s00521-020-05044-w

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  • DOI: https://doi.org/10.1007/s00521-020-05044-w

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