HetRelMTL-Net: A Unified Framework for Knowledge Graph Completion via Graph–Text Fusion and Multi-Task Dynamic Optimization

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.