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New workflow transforms show programs into linked data using VLMs

Researchers have developed a novel workflow to transform historical show programs into structured, interoperable data. This process combines vision-language models (VLMs) for accurate text transcription with a custom extension of the Linked Art ontology for the performing arts. The method was tested using Festival d'Avignon programs from 1947 to the present, achieving over 98% transcription accuracy and significantly reducing semantic annotation hallucinations by constraining large language model generation through a knowledge graph. This approach promises to enable large-scale, interoperable historiography for the performing arts, facilitating the creation of catalogues raisonnés for artists. AI

IMPACT Enables large-scale digital archiving and analysis of performing arts history.

RANK_REASON The item is an academic paper detailing a new methodology for data processing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New workflow transforms show programs into linked data using VLMs

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The item is an academic paper detailing a new methodology for data processing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jeanne Fras ·

    From Show Programs to Linked Data

    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…