现代情报2026，Vol.46Issue(7)：3-16,14.DOI:10.3969/j.issn.1008-0821.2026.07.001
大语言模型驱动的跨学科知识图谱构建与共性知识发现研究
Large Language Model-Driven Construction of Interdisciplinary Knowledge Graphs and Discovery of Common Knowledge Units
摘要
Abstract
[Purpose/Significance]Interdisciplinary integration is vital for addressing complex global challenges,but terminology differences and paradigm fragmentation obscure implicit common knowledge.Existing research relies on explicit connections and fails to identify unassociated commonalities.The paper aims to construct a LLM-driven interdisci-plinary knowledge graph to discover common knowledge and promote cross-field knowledge migration.[Method/Process]The study selected computer science and economics,using the CORE dataset.It applied SciAIEngine to extract core enti-ties,built a"problem-method"semantic system,classified relationships with large language model,and used the HowSim algorithm on a Neo4j-based graph to mine commonalities.[Result/Conclusion]The constructed knowledge graph contains 24 542 nodes and 61 697 edges,from which 19 strong common knowledge pairs were identified.Case verification showed that some models of computer science and economics have cross-disciplinary commonalities.The integrated framework of large language model,knowledge graph with refined semantics and HowSim algorithm effectively realizes implicit commo-nality identification,providing a technical path for interdisciplinary knowledge discovery.关键词
共性知识/知识图谱/大语言模型/跨学科知识发现/基于文献的知识发现Key words
common knowledge/knowledge graph/large language model/interdisciplinary knowledge discovery/literature-based discovery分类
信息技术与安全科学引用本文复制引用
俞超,颜欣杰,郑鑫,徐健..大语言模型驱动的跨学科知识图谱构建与共性知识发现研究[J].现代情报,2026,46(7):3-16,14.基金项目
国家自然科学基金项目"基于知识共通性特征的跨学科知识发现"(项目编号:72374233) （项目编号:72374233）
广东省基础与应用基础研究基金项目"基于科技文献大数据的跨学科类比知识发现研究"(项目编号:2024A1515011778). （项目编号:2024A1515011778）
