From Show Programs to Linked Data: A Generative AI Workflow for Performing Arts Collections
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| 出版日期 | 21 Sep 2026 |
| 內容 | Many heritage institutions hold extensive collections of show programs. Too numerous to be cataloged individually, they remain difficult to access and are still largely undigitized. This paper presents a workflow for transforming such documents into structured, interoperable data by combining vision-language models (VLMs), a custom extension of the Linked Art ontology for the performing arts, and different approaches for automatic semantic data annotation. Using the Festival d’Avignon programs (1947–present), preserved at the Bibliothèque nationale de France, as a case study, we demonstrate how VLMs achieve more than 98% text transcription accuracy from heterogeneous heritage document images and how constraining large language model generation through a knowledge graph significantly reduces hallucinations in semantic annotation. We argue that this workflow opens new possibilities for large-scale, interoperable performing arts historiography, including the creation of catalogues raisonnés for performing artists, a form of scholarship common in visual arts but absent in performing arts studies. |
| 刊名 | Information Technology and Libraries |
| 卷期 | V. 45 N.3 2026 |
| 頁數 | 23 |
| 關鍵字 | Large Language Model (LLM), OCR, Cultural Heritage, Performing arts, Ontology, Knowledge Graph, Linked Data, Semantic annotation |
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