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<p>Heritage digitalization is a rapidly expanding segment of the broader Digital Heritage field, connecting cultural heritage documentation, imaging, 3D modelling, digital preservation, semantic data organization, and online access. This study maps heritage digitalization research through a high-precision open-access corpus derived from OpenAlex using the exact phrase “heritage digitalization” in titles and abstracts. Therefore, the results should be interpreted as evidence on the digitization-oriented segment of Digital Heritage rather than as a comprehensive representation of the entire field.</p><p>A PRISMA-aligned screening pipeline reduced 13,438 records retrieved on 3 December 2025 to 12,983 eligible journal articles covering 1996–2025, including early-access items dated 2026. To support scalable and reproducible analysis, the study applies a hybrid semantic classification workflow combining rule-based NLP dictionaries with zero-shot natural language inference for semantic refinement. Records were assigned to technology clusters and heritage-object domains, enabling Technology × Heritage Type mapping, temporal analysis, national specialization profiling, and macro-regional stratification.</p><p>Technology categories were assigned to 95.6% of papers (<i>n</i> = 12,413), heritage-object domains to 93.9% (<i>n</i> = 12,185), and joint labels on both axes to 90.6% of the corpus (<i>n</i> = 11,766). Zero-shot refinement added 728 technology assignments beyond regex-only tagging, increasing technological coverage from 90.0% to 95.6%. These values indicate semantic coverage and internal consistency, not externally benchmarked classification accuracy. The results show the dominance of Digitization/Imaging by publication volume and the strong citation visibility of 3D/Digital Twins, with growing attention to Data/Semantics, VR/AR/XR, and AI/ML. Geospatial profiling reveals differentiated national specialization: Italy leads in overall output and 3D/Digital Twins, the United Kingdom concentrates on Digitization/Imaging, and the USA shows the strongest relative emphasis on Data/Semantics. The study contributes a reproducible science-mapping pipeline and a structured view of technological, thematic, and geographical patterns in heritage digitalization research. </p>

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Mapping heritage digitalization research through openAlex: a large-scale scientometric analysis with hybrid semantic classification

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Khrystyna Lipianina-Honcharenko

摘要

Heritage digitalization is a rapidly expanding segment of the broader Digital Heritage field, connecting cultural heritage documentation, imaging, 3D modelling, digital preservation, semantic data organization, and online access. This study maps heritage digitalization research through a high-precision open-access corpus derived from OpenAlex using the exact phrase “heritage digitalization” in titles and abstracts. Therefore, the results should be interpreted as evidence on the digitization-oriented segment of Digital Heritage rather than as a comprehensive representation of the entire field.

A PRISMA-aligned screening pipeline reduced 13,438 records retrieved on 3 December 2025 to 12,983 eligible journal articles covering 1996–2025, including early-access items dated 2026. To support scalable and reproducible analysis, the study applies a hybrid semantic classification workflow combining rule-based NLP dictionaries with zero-shot natural language inference for semantic refinement. Records were assigned to technology clusters and heritage-object domains, enabling Technology × Heritage Type mapping, temporal analysis, national specialization profiling, and macro-regional stratification.

Technology categories were assigned to 95.6% of papers (n = 12,413), heritage-object domains to 93.9% (n = 12,185), and joint labels on both axes to 90.6% of the corpus (n = 11,766). Zero-shot refinement added 728 technology assignments beyond regex-only tagging, increasing technological coverage from 90.0% to 95.6%. These values indicate semantic coverage and internal consistency, not externally benchmarked classification accuracy. The results show the dominance of Digitization/Imaging by publication volume and the strong citation visibility of 3D/Digital Twins, with growing attention to Data/Semantics, VR/AR/XR, and AI/ML. Geospatial profiling reveals differentiated national specialization: Italy leads in overall output and 3D/Digital Twins, the United Kingdom concentrates on Digitization/Imaging, and the USA shows the strongest relative emphasis on Data/Semantics. The study contributes a reproducible science-mapping pipeline and a structured view of technological, thematic, and geographical patterns in heritage digitalization research.