面向知识发现的科学实验知识图谱构建研究  

Constructing Scientific Experiment Knowledge Graph for Knowledge Discovery

作　　者：石栖 陈文杰[1,2,3] 胡正银 韩涛[2,4] 张凯 Shi Xi;Chen Wenjie;Hu Zhengyin;Han Tao;Zhang Kai(National Science Library(Chengdu),Chinese Academy of Sciences,Chengdu 610299,China;Department of Information Resources Management,School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China;Sichuan Science and Technology Information Intelligent Mining and Application Engineering Research Center,Chengdu 610299,China;National Science Library,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院成都文献情报中心,成都610299 [2]中国科学院大学经济与管理学院信息资源管理系,北京100190 [3]四川省科技信息智能挖掘与应用工程研究中心,成都610299 [4]中国科学院文献情报中心,北京100190

出　　处：《数据分析与知识发现》2025年第3期1-15,共15页Data Analysis and Knowledge Discovery

基　　金：中国科学院“十四五”文献情报能力建设专项任务(项目编号:E2C0003008)的研究成果之一。

摘　　要：【目的】从科技文献中高效挖掘科学实验知识与数据,构建科学实验知识图谱,为知识发现提供高质量数据支撑。【方法】利用事件知识图谱技术对科学实验的复杂性、时序性及知识和数据融合性等知识对象进行统一的知识表示与建模,构建科学实验知识图谱模式层;利用大语言模型提升科学实验知识图谱数据层的知识抽取效率,并以有机太阳能电池为例进行实证。【结果】采用人工标注与大语言模型微调方式构建了一个有机太阳能电池领域科学实验知识图谱,包含34类节点,9种关系,总计有24348个节点和123642个关系。【局限】数据来源仅包括论文和专利;科学实验知识图谱构建需要较多专家人工参与,效率尚待进一步提高;未考虑细分领域中的细粒度研究规程和研究方法检验规则等。【结论】本文提出的学科领域科学实验知识图谱构建方法可为实验方案推荐、科学实验演化分析、AI for Science等提供高质量数据支持,有效支撑各类知识发现场景。[Objective]This study aims to efficiently extract scientific experiment knowledge and data from academic literature.It constructs a Scientific Experiment Knowledge Graph(SEKG)to provide high-quality data support for knowledge discovery.[Methods]We utilized Event Knowledge Graph technology to uniformly represent and model the complexity,temporality,and integration of knowledge and data in scientific experiments,thereby establishing the schema layer of the SEKG.Large Language Model was employed to enhance the efficiency of knowledge extraction in the data layer,with an empirical analysis conducted on organic solar cells.[Results]By using manual annotation and fine-tuning large language models,we constructed a scientific experiment knowledge graph in the field of organic solar cells.This SEKG comprises 34 types of nodes and 9 types of relationships,totaling 24,348 nodes and 123,642 relations.[Limitations]The data sources were limited to papers and patents.The construction of the SEKG required substantial manual input from experts,highlighting the need for efficiency improvements.Furthermore,fine-grained research procedures and validation rules in subfields were not considered.[Conclusions]The proposed method provides high-quality data support for applications such as experimental protocol recommendations,scientific experiment evolution analysis,and AI for Science,effectively supporting various knowledge discovery scenarios.

关 键 词：科学实验 事件知识图谱 知识发现 大语言模型 

分 类 号：G353[文化科学—情报学] TP391[自动化与计算机技术—计算机应用技术]