What question did this study set out to answer?

The aim is to systematically review the applications of knowledge graphs in cultural heritage, identifying key trends and methodologies.

March 12, 2026Open Access

Knowledge Graph Applications in Cultural Heritage: A ROSES-Based Systematic Review

Key Points

The aim is to systematically review the applications of knowledge graphs in cultural heritage, identifying key trends and methodologies.
Conducted a ROSES-based systematic review of knowledge graph applications in cultural heritage.
Performed a structured search in Scopus, Web of Science, and IEEE Xplore.
Screened and assessed quality of 248 peer-reviewed studies from 2015 to 2024.
Employed a mixed-method approach, integrating descriptive analysis and thematic synthesis.
Identified five main application areas: digital recording and preservation, knowledge management, protection and restoration, cultural education, and research innovation.
Noted a methodological shift from ontology-driven models to hybrid approaches incorporating AI techniques.
Highlighted ongoing challenges such as ontology alignment and limited interoperability across projects.

Abstract

Knowledge Graphs (KGs) are increasingly adopted in cultural heritage research to address challenges of semantic heterogeneity, data fragmentation, and cross-institutional knowledge integration. Despite the rapid growth of KG-based heritage systems, a comprehensive and methodologically rigorous synthesis of existing applications remains limited. To address this gap, this study conducts a ROSES-based systematic review of KG applications in cultural heritage, aiming to examine prevailing application domains, methodological patterns, and emerging research trends. Following the Reporting Standards for Systematic Evidence Syntheses (ROSES), a structured search was conducted in Scopus, Web of Science, and IEEE Xplore. After duplicate removal, screening, eligibility assessment, and quality appraisal, 248 peer-reviewed studies published between 2015 and 2024 were retained for final synthesis. A mixed-method approach combining descriptive analysis and thematic synthesis was employed to analyze KG construction strategies, technological components, application contexts, and reported outcomes. The results indicate that KGs are primarily applied in five interconnected areas: digital recording and preservation, knowledge management and integration, protection and restoration support, cultural transmission and education, and research and innovation. Methodologically, the literature reveals a transition from ontology-driven and manually curated knowledge models toward hybrid approaches integrating artificial intelligence techniques such as natural language processing and machine learning. However, persistent challenges remain, including ontology alignment, scalability, evaluation inconsistency, and limited cross-project interoperability. This review contributes a consolidated and transparent evidence base for KG applications in cultural heritage and advances a conceptual understanding of KGs as socio-technical infrastructures that mediate cultural knowledge representation and interpretation. The findings offer methodological insights and practical implications for researchers, heritage professionals, and system designers, while highlighting directions for future interdisciplinary research.

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