用知识图谱增强材料科学文献检索，提升准确性和可解释性。
G-RAG: Knowledge Expansion in Material Science
- 通过提取材料编号（MatIDs）并连接维基百科知识库，扩展文档信息。
- 基于图数据库建模实体间关系，使检索准确率显著提升。
- 适合需要精准信息的材料科学领域研究者使用。
在材料科学领域，高效的信息检索系统对科研至关重要。传统大语言模型的检索增强生成（RAG）方法常面临信息过时、幻觉、上下文限制导致的可解释性差以及检索不准确等问题。为解决这些问题，图RAG结合图数据库优化检索流程。本文提出的方法从材料科学文档中提取关键实体（称为MatIDs），利用这些实体查询外部维基百科知识库以获取额外相关信息。采用基于代理的解析技术，实现更细致的文档表征。改进版图RAG（G-RAG）进一步利用图数据库捕捉实体间的关联，提升了检索准确率和上下文理解能力。该方法在要求高精度信息检索的材料科学领域表现显著优于传统方案。
原文摘要 · Abstract (English)
In the field of Material Science, effective information retrieval systems are essential for facilitating research. Traditional Retrieval-Augmented Generation (RAG) approaches in Large Language Models (LLMs) often encounter challenges such as outdated information, hallucinations, limited interpretability due to context constraints, and inaccurate retrieval. To address these issues, Graph RAG integrates graph databases to enhance the retrieval process. Our proposed method processes Material Science documents by extracting key entities (referred to as MatIDs) from sentences, which are then utilized to query external Wikipedia knowledge bases (KBs) for additional relevant information. We implement an agent-based parsing technique to achieve a more detailed representation of the documents. Our improved version of Graph RAG called G-RAG further leverages a graph database to capture relationships between these entities, improving both retrieval accuracy and contextual understanding. This enhanced approach demonstrates significant improvements in performance for domains that require precise information retrieval, such as Material Science.
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