用简单查询提升医疗数据匹配效率与可解释性
Schema Matching on Graph: Iterative Graph Exploration for Efficient and Explainable Data Integration
- 通过迭代执行1跳SPARQL查询，避免复杂多跳推理
- 在真实医疗数据集上达到顶尖模型性能，存储开销更低
- 适合需要可解释性与实时更新的医疗数据集成场景
模式匹配是数据集成中的关键任务，尤其在电子健康记录（EHR）系统需对齐标准模型（如OMOP CDM）的医疗领域。尽管大语言模型（LLMs）在该任务中展现出潜力，但存在幻觉和缺乏最新领域知识的问题。知识图谱（KG）提供了结构化、可验证的知识解决方案。然而，现有基于KG的LLM方法通常依赖低效的复杂多跳查询或高存储开销的向量检索。本文提出SMoG（Schema Matching on Graph），一种新颖框架，借鉴知识图谱问答（KGQA）的成功策略，通过迭代执行简单的1跳SPARQL查询，增强可解释性和可靠性，同时直接查询SPARQL端点显著降低存储需求。在真实医疗数据集上的实验表明，SMoG性能可媲美最先进基线，验证了其在知识图谱增强模式匹配中的有效性与高效性。
原文摘要 · Abstract (English)
Schema matching is a critical task in data integration, particularly in the medical domain where disparate Electronic Health Record (EHR) systems must be aligned to standard models like OMOP CDM. While Large Language Models (LLMs) have shown promise in schema matching, they suffer from hallucination and lack of up-to-date domain knowledge. Knowledge Graphs (KGs) offer a solution by providing structured, verifiable knowledge. However, existing KG-augmented LLM approaches often rely on inefficient complex multi-hop queries or storage-intensive vector-based retrieval methods. This paper introduces SMoG (Schema Matching on Graph), a novel framework that leverages iterative execution of simple 1-hop SPARQL queries, inspired by successful strategies in Knowledge Graph Question Answering (KGQA). SMoG enhances explainability and reliability by generating human-verifiable query paths while significantly reducing storage requirements by directly querying SPARQL endpoints. Experimental results on real-world medical datasets demonstrate that SMoG achieves performance comparable to state-of-the-art baselines, validating its effectiveness and efficiency in KG-augmented schema matching.
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