A multi-hierarchical aggregation-based graph convolutional network for industrial knowledge graph embedding towards cognitive intelligent manufacturing

• Main Authors: Liu, Bufan, Chen, Chun-Hsien, Wang, Zuoxu
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Multi-hierarchical aggregation; Graph convolutional network
• Online Access: https://hdl.handle.net/10356/180754
• Institution: Nanyang Technological University

The rapid development and widespread applications of cognitive computing technologies have led to a paradigm shift towards cognitive intelligent development in manufacturing, where knowledge plays an increasingly important role in enabling higher levels of cognition. Knowledge graph (KG) has emerged as one of the essential tools in cognitive intelligent manufacturing and its completion would significantly impact the quality of knowledge. To facilitate effective knowledge management, KG embedding has proven to be an effective approach for KG completion. However, existing models have deficiencies in achieving relation-specific transformations, differentiating the neighbor nodes, and exploiting the intermediate information generated during the KG embedding learning process, which is prone to limit model performance and hinder successful applications. To address these limitations, this paper proposes a new multi-hierarchical aggregation-based graph convolutional network (GCN), consisting of relation-aware, entity-aware, and across-block aggregation. A parallel relation and entity-aware aggregation (PREA) block is established to simultaneously perform relation-specific transformations and entity-differentiated learning. Additionally, an across-block aggregation is constructed to efficiently integrate extracted information from the intermediate stacked block. To demonstrate the effectiveness and superiority of the proposed approach, 3D printing KG is constructed, which is a representative knowledge-intensive industry linking to a variety of aspects like raw materials, adhesion, usages, etc. Extensive experiments are conducted based on the link prediction task. Illustrative examples are provided to reveal the practical implementation of the proposed method, along with technical details and insightful opinions, exhibiting its promising applications.