In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
近年来,通过整合外部知识库来提高大语言模型(LLM)的性能,检索增强生成(RAG)取得了显著的成功。通过引用外部知识库,RAG可以完善LLM输出,从而有效解决幻觉、缺乏领域特定知识和过时信息等问题。然而,数据库中不同实体之间复杂的关系结...近年来,通过整合外部知识库来提高大语言模型(LLM)的性能,检索增强生成(RAG)取得了显著的成功。通过引用外部知识库,RAG可以完善LLM输出,从而有效解决幻觉、缺乏领域特定知识和过时信息等问题。然而,数据库中不同实体之间复杂的关系结构带来了挑战。对此,GraphRAG利用实体之间的结构化信息来实现更精确和全面的检索,捕捉关系知识并促进与上下文相关的更准确的生成。本文概述了GraphRAG相关技术和技术原理,研究了GraphRAG的下游任务、应用领域和评估标准,最后探讨了GraphRAG的未来研究方向,对未来的技术发展趋势进行了展望。In recent years, Retrieval-Augmented Generation (RAG) has achieved remarkable success in enhancing the performance of large language models (LLMs) by integrating external knowledge bases. By referencing external knowledge bases, RAG can refine the outputs of LLMs, effectively addressing issues such as hallucinations, lack of domain-specific knowledge, and outdated information. However, the complex relational structures among different entities in the databases pose challenges. In response, GraphRAG utilizes the structured information between entities to achieve more precise and comprehensive retrieval, capturing relational knowledge and facilitating more accurate context-related generation. This paper outlines the related technologies and technical principles of GraphRAG, examines its downstream tasks, application domains, and evaluation criteria, and finally explores future research directions for GraphRAG, offering insights into the future trends of technological development.展开更多
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
文摘近年来,通过整合外部知识库来提高大语言模型(LLM)的性能,检索增强生成(RAG)取得了显著的成功。通过引用外部知识库,RAG可以完善LLM输出,从而有效解决幻觉、缺乏领域特定知识和过时信息等问题。然而,数据库中不同实体之间复杂的关系结构带来了挑战。对此,GraphRAG利用实体之间的结构化信息来实现更精确和全面的检索,捕捉关系知识并促进与上下文相关的更准确的生成。本文概述了GraphRAG相关技术和技术原理,研究了GraphRAG的下游任务、应用领域和评估标准,最后探讨了GraphRAG的未来研究方向,对未来的技术发展趋势进行了展望。In recent years, Retrieval-Augmented Generation (RAG) has achieved remarkable success in enhancing the performance of large language models (LLMs) by integrating external knowledge bases. By referencing external knowledge bases, RAG can refine the outputs of LLMs, effectively addressing issues such as hallucinations, lack of domain-specific knowledge, and outdated information. However, the complex relational structures among different entities in the databases pose challenges. In response, GraphRAG utilizes the structured information between entities to achieve more precise and comprehensive retrieval, capturing relational knowledge and facilitating more accurate context-related generation. This paper outlines the related technologies and technical principles of GraphRAG, examines its downstream tasks, application domains, and evaluation criteria, and finally explores future research directions for GraphRAG, offering insights into the future trends of technological development.
