ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams

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Computer Science > Artificial Intelligence

arXiv:2607.24707v1 (cs)

[Submitted on 27 Jul 2026]

Title: ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams

Authors: Ali Ansari , Yasmin Mohammadi , Farnoush Nili , Parsa Esmaeilkhani , Longin Jan Latecki , Eduard Dragut

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Abstract: 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 r

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