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Learning Joint Relational Co-evolution in Spatial-Temporal Knowledge Graph for SMEs Supply Chain Prediction

Published: 04 August 2023 Publication History

Abstract

To effectively explore the supply chain relationships among Small and Medium-sized Enterprises (SMEs), some remarkable progress in such a relation modeling problem, especially knowledge graph-based methods have been witnessed during these years. As a typical link prediction task, supply chain prediction can usually predict the unknown future relationship facts between SMEs by utilizing the historical semantic connections between entities in knowledge graphs (KGs). However, it is still a great challenge for existing models as seldom of them can consider both temporal dependency and cooperative correlation of the connectivity pattern along the timeline synergistically. Accordingly, we propose a novel framework to learn joint relational co-evolution in Spatial-Temporal Knowledge Graphs (STKG). Specifically, on the base of the constructed large-scale financial STKG, a multi-view relational sequences mining method is proposed to reveal the semantic information from ontological concepts. Furthermore, a relational co-evolution learning module is also developed to capture the regularity of evolving connectivity patterns from the spatial-temporal view. Meanwhile, a multiple random subspace representation learning layer is also designed to improve both compatibility and complementarity during knowledge aggregation. Experimental results on large-scale SMEs supply chain prediction tasks from four real-world industries in China have illustrated the effectiveness of the proposed model.

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To effectively explore the supply chain relationships among Small and Medium-sized Enterprises (SMEs), we propose a novel framework to learn joint relational co-evolution in Spatial-Temporal Knowledge Graphs (STKG). Specifically, on the base of the constructed large-scale financial STKG, a multi-view relational sequences mining method is proposed to reveal the semantic information from ontological concepts. Furthermore, a relational co-evolution learning module is also developed to capture the regularity of evolving connectivity patterns from the spatial-temporal view. Meanwhile, a multiple random subspace representation learning layer is also designed to improve both compatibility and complementarity during knowledge aggregation.

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Cited By

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  • (2024)GPU Algorithms for Fastest Path Problem in Temporal GraphsProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673105(587-596)Online publication date: 12-Aug-2024
  • (2024)Knowledge Management in SMEs: Applying Link Prediction for Assisted Decision MakingAdvances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond10.1007/978-3-031-67159-3_24(216-225)Online publication date: 1-Aug-2024

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cover image ACM Conferences
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
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Published: 04 August 2023

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Author Tags

  1. deep learning
  2. knowledge graph
  3. supply chain prediction

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View all
  • (2024)GPU Algorithms for Fastest Path Problem in Temporal GraphsProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673105(587-596)Online publication date: 12-Aug-2024
  • (2024)Knowledge Management in SMEs: Applying Link Prediction for Assisted Decision MakingAdvances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond10.1007/978-3-031-67159-3_24(216-225)Online publication date: 1-Aug-2024

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