Relation-aware Subgraph Graph Neural Network for Modeling Document Relevance

In the context of the information age, the exponential growth in the volume of document data makes it challenging to retrieve information quickly and accurate-ly. Traditional keyword-based retrieval methods have limitations and cannot ef-fectively capture the semantic information of a query, leading to irrelevant retriev-al results. To improve the accuracy of retrieval, researchers have started to use knowledge graph KG tools to enhance the matching of document retrieval re-sults, however, direct retrieval using graph structures is limited by exponential complexity and the inability to model distant related documents. To solve this problem, we propose a new information retrieval model, SGDR Subgraph Neu-ral Network-based Graph Representation and Document Retrieval , which utiliz-es relational subgraph neural networks to deeply mine the structural information and semantic associations in document KG. The SGDR models the relevance of documents mainly from semantic relations and local structure in the KG. The ex-perimental results show that the SGDR model outperforms several baseline mod-els on the DocIR dataset, including significant improvements in the key perfor-mance metric AUC. The effectiveness of each module in the model is verified through ablation experiments, and the results emphasize the importance of initial-izing a deep representation of the document knowledge graph.