用注意力机制筛选科研图谱子图，提升论文推荐精准度
Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance
- 基于注意力的图神经网络提取关键子图
- 结合大模型实现科研知识深度推理
- 适合需要高效文献筛选的研究人员
在信息爆炸的时代，科学论文获取虽易，但筛选海量研究内容愈发困难。图神经网络（GNN）与图注意力机制在大规模信息检索中表现优异，尤其与现代大语言模型结合时效果更佳。本文提出一种基于注意力的子图检索器（Attention-Based Subgraph Retriever），该模型利用注意力机制对微软学术图谱（Microsoft Academic Graph）进行剪枝，提取出精炼的子图，并将其输入大语言模型，以实现高级知识推理，从而提升科研推荐与辅助的准确性与效率。
原文摘要 · Abstract (English)
In today's information-driven world, access to scientific publications has become increasingly easy. At the same time, filtering through the massive volume of available research has become more challenging than ever. Graph Neural Networks (GNNs) and graph attention mechanisms have shown strong effectiveness in searching large-scale information databases, particularly when combined with modern large language models. In this paper, we propose an Attention-Based Subgraph Retriever, a GNN-as-retriever model that applies attention-based pruning to extract a refined subgraph, which is then passed to a large language model for advanced knowledge reasoning.
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