发表机构
Faculty of Ocean Science and Technology, Kobe University; Graduate School of Maritime Sciences, Kobe University; Department of Infrastructure Engineering, the University of Melbourne; Center for Mathematical and Data Sciences, Kobe University（神户大学海洋科学技术学部; 神户大学海事科学研究生院; 墨尔本大学基础设施工程系; 神户大学数学与数据科学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用GraphRAG结合GPT-4o mini和Neo4j构建知识图谱，系统整合物理互联网领域文献，识别趋势与知识空白，提升信息可访问性，为PI研究提供新分析工具。
AI 中文摘要
物理互联网（PI）是一个新兴概念，将数字互联网作为设计隐喻，用于发展可持续、可互操作和协作的货运运输。它被认为是推动物流进入下一代转型的一种方式。有效的知识组织和整合工具对于驾驭该领域的新兴研究至关重要。在本研究中，我们探索了图检索增强生成（GraphRAG）在PI背景下的应用，使用GPT-4o mini和Neo4j构建知识图谱，并系统分析现有的PI相关文献。我们的方法综合了分散的研究发现，突出了新兴趋势，并识别了知识空白。此外，我们证明了GraphRAG通过将复杂信息结构化为互联图谱来提高可访问性，并提供了对潜在研究动态的更深入理解。这项研究将通过提供PI领域信息分析的新方法，为未来的研究和创新做出贡献。
英文摘要
Physical Internet (PI) is an emerging concept that applies the digital internet as a design metaphor for the development of sustainable, interoperable, and collaborative freight transportation. It is considered a way to bring logistics into the next generation of transformation. Effective tools for organizing and integrating knowledge are essential for navigating the emerging research in this area. In this study, we explore the application of Graph Retrieval Augmented Generation (GraphRAG) in the context of PI, using GPT-4o mini and Neo4j to construct a knowledge graph and systematically analyze existing PI-related literature. Our approach synthesizes scattered research findings, highlights emerging trends, and identifies knowledge gaps. Furthermore, we demonstrate that GraphRAG improves accessibility by structuring complex information into interconnected graphs and provides a deeper understanding of underlying research dynamics. This research will contribute to future research and innovation by providing a new method of information analysis in the PI domain.
