发表机构
Temple University（天普大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对实体关系图仅为图像限制AI辅助数据库工程的问题，引入ERUnderstand基准测试。通过对2960个图表及标准化表示评估视觉语言模型，发现常见元素恢复良好，但特定元素性能差，推理增强模型可提升性能，为评估多模态理解提供基准。
AI 中文摘要
实体关系图（ERD）是概念数据库设计的核心，但通常仅以渲染图像形式存在，而非机器可读模式，这限制了人工智能辅助数据库工程。我们引入了ERUnderstand，这是首个用于结构化理解ER图的大规模基准测试，包含从精心挑选的教育资源、真实世界模式以及跨越不同领域、符号、复杂度级别和扩展实体关系（EER）构造的合成示例中收集的2960个图表。每个图表都与标准化的机器可读表示配对，用于对模式元素进行细粒度评估。评估当前最先进视觉语言模型（VLM）时发现，虽然常见ERD元素能可靠恢复（F1>0.74），但在弱实体（F1低至0.28）、多值属性（0.14 F1）和N元关系（0.07 F1）上性能大幅下降。推理增强模型将整体性能提高了15%-25%，但仍对语言先验和图表复杂度增加敏感。ERUnderstand为评估概念数据库模式的多模态理解提供了标准化基准。基准测试、数据集、评估工具包和生成代码可在指定网址公开获取。
英文摘要
Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than machine-readable schemas, limiting AI-assisted database engineering. We introduce ERUnderstand, the first large-scale benchmark for structured understanding of ER diagrams, comprising 2,960 diagrams collected from curated educational sources, real-world schemas, and synthetically generated examples spanning diverse domains, notations, complexity levels, and Extended Entity-Relationship (EER) constructs. Each diagram is paired with a standardized machine-readable representation for fine-grained evaluation of schema elements. Evaluating state-of-the-art Vision-Language Models (VLMs), we find that while common ERD elements are recovered reliably (F1 > 0.74), performance drops sharply on weak entities (as low as 0.28 F1), multivalued attributes (0.14 F1), and N-ary relationships (0.07 F1). Reasoning-augmented models improve overall performance by 15-25% but remain sensitive to linguistic priors and increasing diagram complexity. ERUnderstand provides a standardized benchmark for evaluating multimodal understanding of conceptual database schemas. The benchmark, dataset, evaluation toolkit, and generation code are publicly available at https://github.com/salinaria/ERUnderstand.
