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Research on Converter Steelmaking Expert System Based on Knowledge Graph

Published: 03 May 2024 Publication History

Abstract

Converter steelmaking process usually involves many complex processes and technical specifications, and the knowledge in the field of iron and steel smelting exists in the form of documents and databases, which lacks integration and sharing. In order to assist the real-time decision-making of converter steelmaking, this paper investigates the knowledge graph-based expert system for converter steelmaking. In the design of converter steelmaking knowledge graph, a top-down construction method is adopted, in which the ontology level is constructed first, and then the entities and relations are extracted. Where each entity as well as attribute contains the category to which it belongs, the ternary of the conceptual hierarchy (entity category, relation, entity category) can be used to filter the high-quality negative samples as well as the final answer, thus improving the accuracy of the system. In order to improve the interactive capability of the converter steelmaking expert system, this paper adopts the client-server architecture model so as to provide real-time decision support to the operators.

References

[1]
QI Guilin, GAO Huan, WU Tianxing. Advances in knowledge graph research[J]. Intelligence Engineering, 2017, 3(01):4-25.
[2]
Carlson A, Betteridge J, Kisiel B, Toward an Architecture for Never-Ending Language Learning [C]. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010. AAAI
[3]
Suchanek F M, Kasneci G, Weikum A G. Yago-A Large Ontology from Wikipedia and WordNet [J]. Web Semantics Science Services & Agents on the World Wide Web, 2008, 6(3):203-217.
[4]
Xu B, Xu Y, Liang J, CN-DBpedia: A Never-Ending Chinese Knowledge Extraction System [C]. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, Cham, 2017.
[5]
XU Qi, YIN Shaoyang, ZHANG Xingwei Research on carbon emission assessment method for typical industrial fields based on association knowledge mapping [J]. Modern Industrial Economy and Informatization, 2023, 13(10): 31-34.
[6]
Ge Ruifu, Ren Zhigang, Lin Jianghao A knowledge graph construction method and application for process defects of injection molded products [J/OL]. Control Theory and Applications:1-9, 2023, 12, 09. https://kns-cnki-net.wvpn.ncu.edu.cn/kcms/detail/44.1240.TP.20231114.1441.080.html.
[7]
WANG Jing, ZHANG Miao, LIU Yang Research on biannual fusion knowledge graph for process industry control [J]. Computer Science, 2023, 50(09):68-74. Sun Xi. Research on key technologies for vertical domain knowledge graph construction[D]. Beijing University of Posts and Telecommunications, 2019.
[8]
DEVLIN J, CHANG M W, LEE K, BERT: pre-training of deep bidirectional transformers for language understanding [J]. arXiv:1810.04805, 2018.

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  1. Research on Converter Steelmaking Expert System Based on Knowledge Graph

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    IoTAAI '23: Proceedings of the 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence
    November 2023
    902 pages
    ISBN:9798400716485
    DOI:10.1145/3653081
    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 the author(s) 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].

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    Publication History

    Published: 03 May 2024

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    • Research-article
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    Funding Sources

    • Jiangxi Provincial Natural Science Foundation No. 20231ZDE04029 and No. 20224ABC03A01.

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    IoTAAI 2023

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