Abstract
This paper introduces FRAME (Fine-grained Recognition of Art-historical Metadata and Entities), a manually annotated dataset of art-historical image descriptions for Named Entity Recognition (NER) and Relation Extraction (RE). Descriptions were collected from museum catalogs, auction listings, open-access platforms, and scholarly databases, then filtered to ensure that each text focuses on a single artwork and contains explicit statements about its material, composition, or iconography. FRAME provides stand-off annotations in three layers: a metadata layer for object-level properties, a content layer for depicted subjects and motifs, and a co-reference layer linking repeated mentions. Across layers, entity spans are labeled with 37 types and connected by typed RE links between mentions. Entity types are aligned with Wikidata to support Named Entity Linking (NEL) and downstream knowledge-graph construction. The dataset is released as UIMA XMI Common Analysis Structure (CAS) files with accompanying images and bibliographic metadata, and can be used to benchmark and fine-tune NER and RE systems, including zero- and few-shot setups with Large Language Models (LLMs).
Abstract (translated)
本文介绍了FRAME（艺术史元数据和实体的细粒度识别），这是一个由人工注释的艺术史图像描述数据集，用于命名实体识别（NER）和关系抽取（RE）。描述是从博物馆目录、拍卖清单、开放获取平台和学术数据库中收集的，经过筛选以确保每个文本聚焦于单一艺术品，并包含对其材质、构图或象征意义的具体陈述。FRAME提供了三层离散注释：元数据层用于对象级属性；内容层用于描绘的主题和动机；以及共指层连接重复提及的内容。在各层次之间，实体跨度被标记为37种类型并通过提及之间的类型化RE链接进行关联。实体类型与Wikidata对齐以支持命名实体链接（NEL）及下游知识图的构建。该数据集作为UIMA XMI通用分析结构（CAS）文件发布，并附带相关图像和文献元数据，可用于基准测试并微调NER和RE系统，包括大型语言模型（LLMs）的零样本和少样本设置。
URL
https://arxiv.org/abs/2602.19133
