文献衍生聚合物知识的检索增强生成：生物降解聚合物专家系统的例子
Retrieval Augmented Generation of Literature-derived Polymer Knowledge: The Example of a Biodegradable Polymer Expert System
AI总结：
本文提出基于检索增强生成的聚合物知识系统，通过构建结构化知识图谱和语义向量方法，提升生物降解聚合物领域的文献检索与推理能力。
摘要：
聚合物文献包含大量且不断增长的实验知识，但其中大部分信息被无结构文本和不一致的术语所隐藏，这使得系统化的检索和推理变得困难。现有的工具通常孤立地提取狭窄、研究特定的事实，无法保留跨研究所需的上下文，以回答更广泛的科学问题。检索增强生成（RAG）提供了一种有希望的方法，通过将大型语言模型（LLMs）与外部检索相结合来克服这一限制，但其有效性很大程度上取决于领域知识的表示方式。在本工作中，我们开发了两个检索管道：基于密集语义向量的方法（VectorRAG）和基于图的方法（GraphRAG）。使用超过1000篇聚羟基烷酸（PHA）论文，我们构建了保留上下文的段落嵌入和一个标准化的结构化知识图谱，支持实体歧义消除和多跳推理。我们通过标准检索指标、与通用最先进系统如GPT和Gemini的比较以及由领域化学家进行的定性验证来评估这些管道。结果表明，GraphRAG在精度和可解释性方面表现更高，而VectorRAG提供了更广泛的召回，突显了互补的权衡。专家验证进一步确认，经过定制的管道，特别是GraphRAG，能够产生有根据的、引用可靠的响应，具有强领域的相关性。通过将每一个声明都基于证据，这些系统使研究人员能够导航文献，比较研究之间的发现，并揭示难以手动提取的模式。更广泛地说，这项工作建立了一个实用的框架，用于使用经过编纂的语料库和检索设计构建材料科学助手，减少对专有模型的依赖，同时在大规模上实现可信的文献分析。
英文摘要：
Polymer literature contains a large and growing body of experimental knowledge, yet much of it is buried in unstructured text and inconsistent terminology, making systematic retrieval and reasoning difficult. Existing tools typically extract narrow, study-specific facts in isolation, failing to preserve the cross-study context required to answer broader scientific questions. Retrieval-augmented generation (RAG) offers a promising way to overcome this limitation by combining large language models (LLMs) with external retrieval, but its effectiveness depends strongly on how domain knowledge is represented. In this work, we develop two retrieval pipelines: a dense semantic vector-based approach (VectorRAG) and a graph-based approach (GraphRAG). Using over 1,000 polyhydroxyalkanoate (PHA) papers, we construct context-preserving paragraph embeddings and a canonicalized structured knowledge graph supporting entity disambiguation and multi-hop reasoning. We evaluate these pipelines through standard retrieval metrics, comparisons with general state-of-the-art systems such as GPT and Gemini, and qualitative validation by a domain chemist. The results show that GraphRAG achieves higher precision and interpretability, while VectorRAG provides broader recall, highlighting complementary trade-offs. Expert validation further confirms that the tailored pipelines, particularly GraphRAG, produce well-grounded, citation-reliable responses with strong domain relevance. By grounding every statement in evidence, these systems enable researchers to navigate the literature, compare findings across studies, and uncover patterns that are difficult to extract manually. More broadly, this work establishes a practical framework for building materials science assistants using curated corpora and retrieval design, reducing reliance on proprietary models while enabling trustworthy literature analysis at scale.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
