用知识图谱增强大模型调用气象API的能力，提升准确率与推理效率。
Enhancing the Capabilities of Large Language Models for API calls through Knowledge Graphs
- 引入知识图谱作为持久记忆，结合LLM与ReAct Agent实现智能数据查询。
- 在气象领域测试中，API调用正确率达88.57%，显著优于基线方法。
- 适合需要深度领域知识的复杂查询任务，如气象数据分析与集成。
大语言模型（LLMs）通过API调用进行数据分析是一种前沿方法，但在气象等知识密集型领域中，其有效利用工具的能力仍待深入探索。本文提出KG2data系统，融合知识图谱、LLMs、ReAct代理和工具使用技术，实现气象领域的智能数据获取与查询处理。通过虚拟API评估，三个指标显示：名称识别失败率、幻觉失败率和调用正确率分别为1.43%、0%和88.57%，优于RAG2data（16%、10%、72.14%）和chat2data（7.14%、8.57%、71.43%）。该系统突破传统LLM对领域知识访问受限的瓶颈，通过知识图谱增强内容检索、复杂查询处理、领域推理、语义关系解析及异构数据整合能力。同时降低微调成本，提升对动态更新的领域知识与API结构的适应性。总体而言，KG2data为高知识需求领域提供了基于知识的智能问答与数据分析新范式。
原文摘要 · Abstract (English)
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.
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