Knowledge graph completion (KGC) necessitates comprehensive modeling of heterogeneous relations by effectively integrating both graph structural information and textual semantics. Current approaches often exhibit fragmented feature utilization or suboptimal multi-tasking coordination, which limits their capability to handle complex relational patterns such as symmetry, hierarchy, and asymmetry. This paper proposes HetRelMTL-Net, a unified framework that introduces two key innovations: (1) GraphBert-KGC, a graph–text fusion module that dynamically aligns structural and semantic features through relation-aware attention and adaptive gating mechanisms, achieving a 97% reduction in parameter redundancy compared to fixed-projection baselines; and (2) a multi-task learning architecture that jointly optimizes link prediction, relation classification, and path reasoning via a KL-divergence-based dynamic weighting strategy, effectively mitigating task conflicts while enhancing semantic discriminability. Extensive experiments on two publicly available benchmarks, WN18RR and FB15k-237, demonstrate state-of-the-art performance: achieving 28.7% Hits@1 on asymmetric relations (a 19.1% improvement over RotatE), an 85.8% higher MRR than TransE, and 12% faster convergence. The robustness and efficacy of the framework are further validated through detailed ablation studies, attention visualizations, and real-world case analyses.
Wu et al. (Sat,) studied this question.