Link prediction for static knowledge graphs based on attention mechanism and deep graph propagation

The ongoing expansion of application scenarios for knowledge graphs enhances the research significance of link prediction. Current graph neural network methodologies utilized for link prediction on static knowledge graphs frequently suffer from node feature over-smoothing and information degradation in deeper layers, hindering the comprehensive utilisation of structural patterns and semantic relationships within the graphs. This study aims to overcome these limitations by focusing on the static knowledge graph completion problem and proposing a link prediction method that incorporates attention mechanisms and deep graph propagation. This method enhances the efficacy of link prediction by optimising semantic graph representations and analysing entity relationships. This paper’s primary contribution is a sophisticated graph propagation model called Multi-Head Attention and Dynamic Pruning Graph (MADPGraph), designed for static knowledge graph link prediction, integrating multi-head attention relation-aware modules and dynamic pruning techniques. The approach initially creates query-oriented subgraphs to encapsulate multi-hop neighbourhood contextual information focused on head entities. A relation-aware multi-head attention technique aggregates query relations, edge relations, and node features, enabling deep information propagation across multiple attention heads. An inter-layer adaptive dynamic pruning module is introduced after generating contextualised node representations for each query. This module dynamically identifies semantically pertinent nodes during layer-wise propagation to limit subgraph size and is designed to reduce the risk of feature homogenization caused by irrelevant deep propagation. It retains semantic information along essential pathways and improves model generalisation by ensuring uniform representations across various trimmed paths. Experimental findings on typical static knowledge graph datasets and inductive benchmark datasets indicate that MADPGraph achieves competitive performance relative to existing mainstream approaches, thereby supporting the effectiveness and generalisation ability of the proposed strategy in static contexts.