Comprehensive analysis maps growth and key trends in BIM and semantic data modeling, highlighting gaps.
The integration of Building Information Modeling (BIM) with semantic web technologies is crucial for addressing data interoperability and quality assurance challenges in the AEC industry. This paper presents a comprehensive bibliometric analysis of 234 documents from the Scopus database (2006-2024) to map the intellectual landscape of BIM and semantic data modeling using graph-based RDF representations. The analysis reveals a significant growth in publications, identifying key authors, countries, and the Automation in Construction journal as a leading publication venue. A co-occurrence network analysis delineates distinct research clusters, while trend analysis highlights a recent shift towards Artificial Intelligence (AI), machine learning, and natural language processing. By providing a structured overview of the field and pinpointing critical knowledge gaps, this study offers a valuable roadmap for future research into more intelligent, data-driven construction workflows.
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Silva et al. (2025) studied this question.
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