Fusing Multi-Layer Graph Attention Networks and Bidirectional Path Reasoning for Knowledge Graph Completion
Year: 2025
Type: article
Abstract: To address the insufficient collaborative modeling of local structural information and global path dependencies in knowledge graph completion tasks, we propose MGAT-BiKR, a model that combines multi-layer graph attention networks with bidirectional path reasoning. The multi-layer graph attention network dynamically aggregates the multi-hop neighborhood semantics of target entities. By leveraging a multi-head attention mechanism, it adaptively assigns weights to heterogeneous neighbors, thereby resolving the limitation of traditional methods in identifying key relational contexts. Simultaneously, bidirectional path reasoning is achieved via a BiLSTM encoder combined with path attention, which explicitly captures temporal dependencies in both forward and backward directions across multi-step relational paths. Experiments on the FB15k-237, WN18RR, and NELL995 datasets show that the proposed model achieves Hits@1 scores of 0.986, 0.978, and 0.967 in link prediction tasks. It outperforms the PathCon model by 1.2%, 1.8%, and 12.1%, respectively. This approach efficiently addresses long-tail relationship reasoning in sparse knowledge graphs.
Author: <p>Ao Guo, Guiping Wang, Yingchuan Zhou</p>
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413065860