A heterogeneous graph neural network based entity relationship extraction method in automotive parts supply chain

• Published in: Expert systems with applications Vol. 293; p. 128705
• Main Authors: Li, Xiaobin, Tang, Jianguo, Jiang, Pei, He, Yan, Yin, Chao, Wang, Xi Vincent
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2025
• Subjects: Automotive parts supply chain; Feature fusion; Heterogeneous graph neural network; Joint Entity relation extraction; Feature fusion; Automotive parts supply chain; Joint Entity relation extraction; Heterogeneous graph neural network
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2025.128705

•We introduce for the first time a heterogeneous graph neural network integrating entity relationship to find node representation suitable for the (APSC) ERE task.•A subject feature fusion method based on attention mechanisms is developed to achieve end-to-end triplet extraction.•Experimental results demonstrate outstanding performance on practical supply chain scenarios. [Display omitted] In the huge automotive service aftermarket, the efficient and timely supply of maintenance parts has always attracted continuous concern from automotive service enterprises and end users,which is limited by the regional collaborative efficiency and information interaction among many suppliers over the automotive parts supply networks. However, the supply networks consist of enterprises with different manufacturing capabilities, and are filled with multisource, massive, heterogeneous information that contains multiple entities and overlapping triplet relation, leading to difficulties in achieving uniform representation and adaptive understanding of information. Entity-relation extraction is essential for unified information representation.In this paper, we devise an entity relationship extraction(ERE) method based on heterogeneous graph neural networks and entity feature fusion, which treats entities and relation as nodes in a graph, and iteratively integrates node representation to identify the most suitable node features for ERE tasks. The method introduces an innovative mechanism: firstly, we extract the subject entities and fuse their features into node representations using an attention mechanism; and then, the relations and object entities are jointly extracted to achieving end-to-end triplet extraction. Experiments are conducted using parts supply chain data from partners. The results validates the effectiveness of the method and obtain outstanding performance in the automotive parts supply chain(APSC) networks.