用大模型从论文中提取污染源到健康危害的多跳关系链
Decoding Plastic Toxicity: An Intelligent Framework for Conflict-Aware Relational Metapath Extraction from Scientific Abstracts
- 基于大模型识别科学摘要中的实体关系，构建多跳语义路径
- 生成毒性传播图谱，追踪污染物在环境与生物体中的传递路径
- 动态冲突消解模块提升结果可靠性，适合环境健康研究者使用
塑料的广泛使用及其在环境中持久存在，导致微塑料和纳米塑料在空气、水和土壤中累积，引发呼吸系统、消化系统及神经系统等健康风险。本文提出一种新框架，利用大语言模型从科学摘要中提取关联性元路径——即连接污染源与健康影响的多跳语义链。系统能跨不同语境识别并关联实体，构建结构化的关系元路径，并聚合为毒性轨迹图，追踪污染物通过暴露途径和生物系统传播的过程。此外，为保障一致性与可靠性，引入动态证据协调模块，解决因研究进展或结论矛盾引发的语义冲突。该方法在从嘈杂科学文本中提取可靠、高价值关系知识方面表现优异，为领域特定语料中复杂因果结构挖掘提供了可扩展解决方案。
原文摘要 · Abstract (English)
The widespread use of plastics and their persistence in the environment have led to the accumulation of micro- and nano-plastics across air, water, and soil, posing serious health risks including respiratory, gastrointestinal, and neurological disorders. We propose a novel framework that leverages large language models to extract relational metapaths, multi-hop semantic chains linking pollutant sources to health impacts, from scientific abstracts. Our system identifies and connects entities across diverse contexts to construct structured relational metapaths, which are aggregated into a Toxicity Trajectory Graph that traces pollutant propagation through exposure routes and biological systems. Moreover, to ensure consistency and reliability, we incorporate a dynamic evidence reconciliation module that resolves semantic conflicts arising from evolving or contradictory research findings. Our approach demonstrates strong performance in extracting reliable, high-utility relational knowledge from noisy scientific text and offers a scalable solution for mining complex cause-effect structures in domain-specific corpora.
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