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An improved model combining knowledge graph and GCN for PLM knowledge recommendation

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Abstract

Faced with the challenges of intelligently managing the entire product lifecycle, traditional management techniques no longer meet the complex demands of modern enterprises. To address it, we employ an ontology modeling method based on a multidimensional view of product information for a knowledge graph, culminating in a comprehensive PLM knowledge system. To better serve PLM users, we propose an intelligent recommendation model based on context and user behavior for knowledge recommendations. This model seamlessly integrates the knowledge storage structure, user preference attributes, and the rich semantics of the knowledge graph. To improve the recommendation capability of the model, we design an improved multi-hop neighbor node sampling algorithm and use a graph convolutional neural network to quantify the target user preference attributes layer by layer. After a series of rigorous validation experiments, our method consistently outperformed competing models across key performance metrics, such as accuracy and recall, underscoring its efficacy and practicality in knowledge recommendation within PLM systems.

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The data used in this study are generated by the author’s independent experiment.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China under Grant No. 2018YFB1700902.

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The authors have not disclosed any funding.

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Author notes

  1. Deyun Li and Xuemei Liu have contributed equally to this work.

Authors and Affiliations

  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Guoxiang Tong & Deyun Li

  2. School of Mechanical Engineering, Tongji University, Shanghai, 200092, China

    Xuemei Liu

Authors
  1. Guoxiang Tong
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  2. Deyun Li
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  3. Xuemei Liu
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Contributions

GT, DL, XL: conceptualization, methodology, Validation, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.

Corresponding author

Correspondence to Xuemei Liu.

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Tong, G., Li, D. & Liu, X. An improved model combining knowledge graph and GCN for PLM knowledge recommendation. Soft Comput 28, 5557–5575 (2024). https://doi.org/10.1007/s00500-023-09340-0

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