标题： 通过知识图谱增强大型语言模型的API调用能力 显示英文标题

标题： Enhancing the Capabilities of Large Language Models for API calls through Knowledge Graphs

摘要： 大型语言模型（LLMs）的API调用为数据分析提供了一种前沿的方法。 然而，在像气象学这样的知识密集型领域中，它们通过API调用有效利用工具的能力仍鲜有研究。 本文介绍了KG2data，这是一个集成知识图谱、LLMs、ReAct代理和工具使用技术的系统，旨在实现气象领域的智能数据获取和查询处理。 使用虚拟API，我们通过三个指标评估API调用的准确性：名称识别失败、幻觉失败和调用正确性。 与RAG2data（16%，10%，72.14%）和chat2data（7.14%，8.57%，71.43%）相比，KG2data表现出卓越的性能（1.43%，0%，88.57%）。 KG2data不同于典型的基于LLM的系统，因为它解决了它们在领域特定知识访问受限的问题，这会影响复杂或术语丰富的查询上的性能。 通过使用知识图谱作为持久记忆，我们的系统增强了内容检索、复杂查询处理、领域特定推理、语义关系解析和异构数据集成。 它还降低了微调LLM的高成本，使系统更能适应不断变化的领域知识和API结构。 总之，KG2data为高知识需求领域的智能、基于知识的问答和数据分析提供了新颖的解决方案。 显示英文摘要

摘要： API calls by large language models (LLMs) offer a cutting-edge approach for data analysis. However, their ability to effectively utilize tools via API calls remains underexplored in knowledge-intensive domains like meteorology. This paper introduces KG2data, a system that integrates knowledge graphs, LLMs, ReAct agents, and tool-use technologies to enable intelligent data acquisition and query handling in the meteorological field. Using a virtual API, we evaluate API call accuracy across three metrics: name recognition failure, hallucination failure, and call correctness. KG2data achieves superior performance (1.43%, 0%, 88.57%) compared to RAG2data (16%, 10%, 72.14%) and chat2data (7.14%, 8.57%, 71.43%). KG2data differs from typical LLM-based systems by addressing their limited access to domain-specific knowledge, which hampers performance on complex or terminology-rich queries. By using a knowledge graph as persistent memory, our system enhances content retrieval, complex query handling, domain-specific reasoning, semantic relationship resolution, and heterogeneous data integration. It also mitigates the high cost of fine-tuning LLMs, making the system more adaptable to evolving domain knowledge and API structures. In summary, KG2data provides a novel solution for intelligent, knowledge-based question answering and data analysis in domains with high knowledge demands.