Enhanced Graph Representation Learning for Link Prediction in Human–Machine Collaborative Assembly Knowledge Graphs

Human–robot collaborative assembly relies on structured knowledge of components, assembly operations and process constraints. We propose a knowledge graph representation learning framework to predict missing links in assembly knowledge. A human–robot collaborative assembly knowledge graph is constructed through joint entity–relation extraction, entity alignment and manual verification. Two adaptive relational graph attention network encoders learn structural and semantic representations, which are fused through element-wise addition. A local–global mutual information objective guides structural representation learning, while a Fourier Kolmogorov–Arnold network decoder scores candidate triples. Link prediction experiments on WN18RR, FB15K-237 and the domain-specific HRKG examine the contributions of the model components through ablation. The complete model achieves mean reciprocal ranks of 0.498, 0.382 and 0.902, respectively. An assembly assistance system integrates visual recognition, link prediction, and robotic grasping and delivery. In 600 gearbox assembly subtasks, component requirement prediction was correct in 576 of 594 trials with correct target recognition (97.0%), and the complete workflow succeeded in 550 trials (91.7%). The stage-specific evaluation distinguishes prediction performance from system-level task completion.