Xianghui Meng
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.
- QI Guilin, GAO Huan, WU Tianxing. Advances in knowledge graph research[J]. Intelligence Engineering, 2017, 3(01):4-25.Google Scholar
- 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. AAAIGoogle Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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. DOI:10.16525/j.cnki.14-1362/n.2023.10.010.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google Scholar
- DEVLIN J, CHANG M W, LEE K, BERT: pre-training of deep bidirectional transformers for language understanding [J]. arXiv:1810.04805, 2018.Google Scholar
Index Terms
- Research on Converter Steelmaking Expert System Based on Knowledge Graph
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