PDF(1986 KB)
Experimental Scheme Recommendation Based on Knowledge Graph： A Case Study of Organic Solar Cells
Kai Zhang, Qi Shi
Knowledge Management Forum ›› 2024, Vol. 9 ›› Issue (5) : 448-459.
PDF(1986 KB)
PDF(1986 KB)
Experimental Scheme Recommendation Based on Knowledge Graph： A Case Study of Organic Solar Cells
[Purpose/Significance] Currently, the paradigm of data-intensive scientific discovery in scientific research is evolving towards intelligence. AI-driven scientific research (AI4Science) is becoming the engine of technological innovation and a new paradigm for scientific research. This study will delve into the experimental schemes within AI4Science, revealing the application of AI technology in research methods, tools, and means through the analysis of experimental schemes in scientific literature, providing new research perspectives for scientific researchers. [Method/Process] ① Knowledge extraction and modeling of the experimental scheme. Ontology modeling technology was used to realize the unified knowledge modeling of experimental methods and experimental principles in different subject areas. The domain knowledge graph of organic solar cells was constructed on the basis of ontology modeling. ② Research on intelligent recommendation of the experimental scheme based on the knowledge graph. The relationship between entities was mined in the domain knowledge graph to realize the intelligent recommendation of the experimental scheme. [Result/Conclusion] On the basis of Graph2vec representation technology and GPT embedding representation, GraphGPT Net is proposed as a new algorithm for knowledge graph semantic integration of the experimental scheme. The best performance is achieved on Recall@20 with a score of 0.0299, which proves its remarkable ability to recommend experimental schemes.
knowledge graph / organic solar cells / experimental scheme / recommendation system
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张 凯：撰写论文，基于论文数据进行技术分析；
石 栖：构建数据库，提出修改意见。
感谢中国科学院成都文献情报中心胡正银老师的团队对本研究在数据和技术上的大力支持。
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