Abstract: Knowledge graph embedding is widely applied in fields such as natural language processing and recommendation systems.However,when traditional methods process entities associated through multi-hop relations,they struggle to accurately mo-del the cumulative semantics of multi-hop relational paths,which easily leads to semantic decay and neglects global structural features,making it difficult to capture deep associations.Moreover,redundant entities in long-distance paths not only increase computational burden but also obscure the roles of key entities,interfering with the accuracy of semantic capture and resulting in poor performance in multi-step reasoning tasks.Addressing the over-compression issue of existing methods such as KnowFormer and the shallow network limitation of HittER,this paper proposes a unified framework that simultaneously solves long-distance semantic propagation and efficient neighbor aggregation.Compared with existing methods,this paper innovatively integrates hiera-rchical path modeling with multi-head attention mechanisms in depth,achievs linear complexity optimization from O(｜V｜²).Addressing the insufficient modeling of long-distance relational paths,this paper proposes a MHA-HRPM(Multi-Head Self-Attention enhanced Hierarchical Relational Path Modeling) method.The core innovations include:Firstly,hierarchical relational path modeling,which through hierarchical construction and layer-by-layer interaction,enables path representations to simultaneously preserve their own features and enhanced features from upper layers,achieving layer-wise propagation and precise capture of long-distance path semantics;Secondly,the introduction of a multi-head self-attention mechanism,which dynamically adjusts neighbor entity weights from multiple perspectives,adaptively selects key neighbors,implicitly captures high-order interaction features,and enriches the semantic diversity of entity representations.Experimental results demonstrate that the proposed method exhibits excellent performance in modeling long-distance relational path dependencies.

Key words: Knowledge graph embedding, Long distance relations, Hierarchical relation paths, Attention, Semantic capture