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Knowledge Retrieval Model Based on a Graph Database for Semantic Search in Equipment Purchase Order Specifications for Steel Plants

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  • Ho-Jin Cha

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Republic of Korea
    Kwangyang Rolling Mill Automation Group, POSCO ICT, 68 Hodong-ro, Nam-Ku, Pohang 37861, Republic of Korea)

  • So-Won Choi

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Republic of Korea)

  • Eul-Bum Lee

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Republic of Korea
    Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Republic of Korea)

  • Duk-Man Lee

    (AI (Artificial Intelligence) Research & Development Institute, POSCO Holdings, 440 Teheran-ro, Gangnam-gu, Seoul 06194, Republic of Korea)

Abstract

The complexity and age of industrial plants have prompted a rapid increase in equipment maintenance and replacement activities in recent years. Consequently, plant owners are challenged to reduce the process and review time of equipment purchase order (PO) documents. Currently, traditional keyword-based document search technology generates unintentional errors and omissions, which results in inaccurate search results when processing PO documents of equipment suppliers. In this study, a purchase order knowledge retrieval model (POKREM) was designed to apply knowledge graph (KG) technology to PO documents of steel plant equipment. Four data domains were defined and developed in the POKREM: (1) factory hierarchy, (2) document hierarchy, (3) equipment classification hierarchy, and (4) PO data. The information for each domain was created in a graph database through three subprocesses: (a) defined in a hierarchical structure, (b) classified into nodes and relationships, and (c) written in triples. Ten comma-separated value (CSV) files were created and imported into the graph database for data preprocessing to create multiple nodes. Finally, rule-based reasoning technology was applied to enhance the model’s contextual search performance. The POKREM was developed and implemented by converting the Neo4j open-source graph DB into a cloud platform on the web. The accuracy, precision, recall, and F1 score of the POKREM were 99.7%, 91.7%, 100%, and 95.7%, respectively. A validation study showed that the POKREM could retrieve accurate answers to fact-related queries in most cases; some incorrect answers were retrieved for reasoning-related queries. An expert survey of PO practitioners indicated that the PO document review time with the POKREM was reduced by approximately 40% compared with that of the previous manual process. The proposed model can contribute to the work efficiency of engineers by improving document search time and accuracy; moreover, it may be expandable to other plant engineering documents, such as contracts and drawings.

Suggested Citation

  • Ho-Jin Cha & So-Won Choi & Eul-Bum Lee & Duk-Man Lee, 2023. "Knowledge Retrieval Model Based on a Graph Database for Semantic Search in Equipment Purchase Order Specifications for Steel Plants," Sustainability, MDPI, vol. 15(7), pages 1-37, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6319-:d:1117621
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    References listed on IDEAS

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    1. Yeu-Shiang Huang & Rong-Shuan Ho & Chih-Chiang Fang, 2015. "Quantity discount coordination for allocation of purchase orders in supply chains with multiple suppliers," International Journal of Production Research, Taylor & Francis Journals, vol. 53(22), pages 6653-6671, November.
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    3. Haichao Huang & Zhouzhenyan Hong & Huiming Zhou & Jiaxian Wu & Ning Jin, 2020. "Knowledge Graph Construction and Application of Power Grid Equipment," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, October.
    4. Stefan Bock & Filiz Isik, 2015. "A new two-dimensional performance measure in purchase order sizing," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 4951-4962, August.
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