让多语言问句生成SPARQL查询更智能，像人一样分步思考。
Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning
- 用模块化代理系统分步完成规划、实体链接和查询优化
- 在双语知识图谱基准上取得冠军，准确率显著领先
- 适合需要多语言智能问答的开发者与研究者
通过多语言自然语言接口访问知识是信息检索领域的新兴挑战。知识图谱中的结构化知识可通过特定查询语言（如SPARQL）进行检索，因此需将自然语言输入转化为查询以满足信息需求。以往方法多采用组合式组件（如基于规则或神经网络）在末端输出答案。本文提出mKGQAgent，一种类人推理框架，将自然语言转SPARQL的过程分解为可解释的子任务。该框架通过协调的LLM代理工作流实现规划、实体链接与查询精炼，并借助经验池进行上下文学习，高效处理多语言知识图谱问答。在Text2SPARQL挑战2025的DBpedia与Corporate基准上，本方法位列第一。本工作为构建类人推理的多语言语义解析系统开辟了新路径。
原文摘要 · Abstract (English)
Accessing knowledge via multilingual natural-language interfaces is one of the emerging challenges in the field of information retrieval and related ones. Structured knowledge stored in knowledge graphs can be queried via a specific query language (e.g., SPARQL). Therefore, one needs to transform natural-language input into a query to fulfill an information need. Prior approaches mostly focused on combining components (e.g., rule-based or neural-based) that solve downstream tasks and come up with an answer at the end. We introduce mKGQAgent, a human-inspired framework that breaks down the task of converting natural language questions into SPARQL queries into modular, interpretable subtasks. By leveraging a coordinated LLM agent workflow for planning, entity linking, and query refinement - guided by an experience pool for in-context learning - mKGQAgent efficiently handles multilingual KGQA. Evaluated on the DBpedia- and Corporate-based KGQA benchmarks within the Text2SPARQL challenge 2025, our approach took first place among the other participants. This work opens new avenues for developing human-like reasoning systems in multilingual semantic parsing.
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