用SPARQL-MCP让智能体跨知识图谱问答，提升复杂查询能力
Agentic SPARQL: Evaluating SPARQL-MCP-powered Intelligent Agents on the Federated KGQA Benchmark
- 通过SPARQL-MCP协议连接外部数据源，实现智能体自动规划查询
- 在联邦知识图谱问答基准上验证了智能体的端点发现与查询融合能力
- 适合研究智能体、知识图谱和多源数据集成的学者
标准协议如模型上下文协议（MCP）使大模型能调用工具，推动了以规划能力为核心的“智能体”应用发展。在此背景下，公开的SPARQL端点可通过MCP实现标准化连接：具备统一查询语言、元数据格式及原生联邦查询能力。本文探索基于SPARQL-MCP的智能体在联邦知识图谱问答中的潜力：首先，将现有知识图谱问答基准扩展为支持智能体的联邦知识图谱问答（FKGQA）；其次，实现并评估了通过MCP整合SPARQL联邦查询的能力，涵盖端点发现、源选择、模式探索与查询生成，并对比了不同架构方案。本工作拓展了自动化SPARQL联邦查询的研究方向，推动其与智能体人工智能的深度融合。
原文摘要 · Abstract (English)
Standard protocols such as the Model Context Protocol (MCP) that allow LLMs to connect to tools have recently boosted "agentic" AI applications, which, powered by LLMs' planning capabilities, promise to solve complex tasks with the access of external tools and data sources. In this context, publicly available SPARQL endpoints offer a natural connection to combine various data sources through MCP by (a) implementing a standardised protocol and query language, (b) standardised metadata formats, and (c) the native capability to federate queries. In the present paper, we explore the potential of SPARQL-MCP-based intelligent agents to facilitate federated SPARQL querying: firstly, we discuss how to extend an existing Knowledge Graph Question Answering benchmark towards agentic federated Knowledge Graph Question Answering (FKGQA); secondly, we implement and evaluate the ability of integrating SPARQL federation with LLM agents via MCP (incl. endpoint discovery/source selection, schema exploration, and query formulation), comparing different architectural options against the extended benchmark. Our work complements and extends prior work on automated SPARQL query federation towards fruitful combinations with agentic AI.
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