Reading relevant scientific papers and analyzing research development trends is a critical step in generating new scientific ideas. However, the rapid increase in the volume of research literature and the complex citation relationships make it difficult for researchers to quickly analyze and derive meaningful research trends. The development of large language models (LLMs) has provided a novel approach for automatically summarizing papers and generating innovative research ideas. However, existing paper-based idea generation methods either simply input papers into LLMs via prompts or form logical chains of creative development based on citation relationships, without fully exploiting the semantic information embedded in these citations. Inspired by knowledge graphs and human cognitive processes, we propose a framework called the Graph of AI Ideas (GoAI) for the AI research field, which is dominated by open-access papers. This framework organizes relevant literature into entities within a knowledge graph and summarizes the semantic information contained in citations into relations within the graph. This organization effectively reflects the relationships between two academic papers and the advancement of the AI research field. Such organization aids LLMs in capturing the current progress of research, thereby enhancing their creativity. Experimental results demonstrate the effectiveness of our approach in generating novel, clear, and effective research ideas.

本研究解决了研究文献快速分析及研究趋势提取的挑战，提出了一种名为AI思想图（GoAI）的框架，旨在优化大型语言模型的创意生成过程。通过将相关文献组织成知识图谱中的实体，并提炼引用中的语义信息，该框架增强了大型语言模型对当前研究进展的理解，从而有效生成新颖且清晰的研究想法。

AI思想图：利用知识图谱和大型语言模型进行AI研究的创意生成