A multihop knowledge graph reasoning approach incorporating semantic attention and adaptive reward mechanisms

We present SAAR (Semantic-Aware Reinforcement), a unified reinforcement-based framework for multi-hop knowledge graph inference that addresses sparse data representation and suboptimal path selection. The framework features two key innovations: 1) Contextual path encoding via bidirectional LSTM with attention gates that dynamically prune irrelevant triplets using cosine similarity between relations, reducing search space complexity ; 2) Heterogeneous reward optimization combining entity alignment accuracy and semantic coherence with an exponential decay schedule to prioritize high-quality path exploration.Extensive evaluation across three benchmarks (WN18RR, FB15K-237, NELL-995) confirms SAAR's superiority, achieving state-of-the-art MRR and higher Hits@10 compared to baseline methods. Notably, the model generates interpretable attention patterns that prioritize semantically relevant edges , ensuring both accuracy and path validity