基于知识图谱支持科研创新的跨学科知识发现研究

Research on Interdisciplinary Knowledge Discovery Based on Knowledge Graph to Support Scientific Research Innovation

[目的/意义]跨学科交叉融合正在成为科技创新的重要方式。利用知识图谱进行跨学科知识组织、揭示潜在的知识关联,为跨学科借鉴的科研创新提供有力支持。[方法/过程]选取图书馆学与情报学、传播学、计算机科学、科学学领域的中文学术论文为样本,利用SciAIEngine平台抽取能够反映论文研究问题、研究方法、理论原理等的科研实体及实体关系,构建领域知识图谱。然后,通过相似度计算发现不同学科之间潜在的知识关联,构建跨学科融合知识图谱,提供科研创新知识和情报。[结果/结论]研究发现,基于知识图谱的方法能够有效地从跨学科角度发现新的研究方向、思路和方法,有助于激发科研创新活力。

[Purpose/significance]Interdisciplinary integration is becoming an important way of scientific and technological innovation.The knowledge graph is used to organize interdisciplinary knowledge,reveal potential knowledge associations,and provide strong support for scientific research innovations that are referenced across disciplines.[Method/process]Select Chinese academic papers in the fields of library and information science,communication,computer science,and science as samples,and use the SciAIEngine platform to extract scientific research entities and entity relationships that can reflect the research issues,research methods,theoretical principles to construct a domain knowledge graph.Then,use the similarity calculation method to discover the potential knowledge associations between different disciplines,and build an interdisciplinary fusion knowledge graph to provide scientific research innovation knowledge and intelligence.[Result/conclusion]The study found that the method based on knowledge graph can effectively discover new research directions,ideas and methods from an interdisciplinary perspective,which is helpful to stimulate the vitality of scientific research innovation.