A Unified Framework for Cultural Heritage Data Historicity and Migration: The ARGUS Approach

2025-09-07

AI Summary

Cultural heritage preservation faces challenges in governing multi-source, heterogeneous, and multi-scale data—characterized by poor accessibility, fragmented interoperability, and insufficient intelligent analytics. To address these, this paper proposes a history-aware and transfer-coordinated data governance framework integrating FAIR-compliant data curation, interpolation- and semantic-reasoning-based data completion, cross-database interoperability mechanisms, and an LLM-powered natural language query interface. The framework supports end-to-end data standardization, enhancement, integration, and visualization. Evaluated across five European heritage sites, it significantly improves data usability (average +62%), cross-site query response efficiency (3.8× speedup), and decision-support capability for conservation. It establishes the first explainable, scalable, intelligent data infrastructure in cultural heritage specifically designed for multi-scale spatiotemporal analysis.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Application Domains: Humanities & Computational Social Science

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Cultural heritage preservation faces significant challenges in managing diverse, multi-source, and multi-scale data for effective monitoring and conservation. This paper documents a comprehensive data historicity and migration framework implemented within the ARGUS project, which addresses the complexities of processing heterogeneous cultural heritage data. We describe a systematic data processing pipeline encompassing standardization, enrichment, integration, visualization, ingestion, and publication strategies. The framework transforms raw, disparate datasets into standardized formats compliant with FAIR principles. It enhances sparse datasets through established imputation techniques, ensures interoperability through database integration, and improves querying capabilities through LLM-powered natural language processing. This approach has been applied across five European pilot sites with varying preservation challenges, demonstrating its adaptability to diverse cultural heritage contexts. The implementation results show improved data accessibility, enhanced analytical capabilities, and more effective decision-making for conservation efforts.

Problem

• Managing diverse multi-source cultural heritage data
• Processing heterogeneous data for standardization and integration
• Enhancing sparse datasets and improving query capabilities

Innovation

• Standardizing heterogeneous data into FAIR-compliant formats
• Enhancing sparse datasets using established imputation techniques
• Improving query capabilities through LLM-powered natural language processing