In recent decades, the construction industry has made significant steps towards digitalisation. One of the most persistent challenges remains ensuring data accuracy, particularly in building component classification. Recent research has explored the application of machine learning algorithms for element classification within Building Information Modelling (BIM). These methods, while effective in handling individual geometric features, often overlook the critical influence of an element’s spatial context, leading to misclassifications in dense and interconnected models. To address this gap, ContextNet proposes an innovative deep-learning approach that integrates contextual information from surrounding components, enhancing classification accuracy by accounting for spatial relationships. By processing point clouds from both individual elements and their context, ContextNet enables more robust identification of building components within BIM models. Results demonstrate that ContextNet significantly improves classification accuracy and robustness, reducing common misclassifications in complex BIM datasets. The proposed framework enhances BIM data by enabling context-aware component recognition, supporting more accurate and dependable classification within the construction industry. • A neural network for context-aware classification of components in BIM. • Enhances object recognition accuracy by incorporating spatial context from elements. • Uses context to improve accuracy, reducing errors in building model classification.

Despite decades of the digitization of the construction industry, interoperability challenges prevail. One of which is the lack of sufficient integration between domain-specific BIM models. Therefore, the paper proposes a novel methodology for federating cross-domain BIM-based knowledge graphs. The approach leverages the integration of multiple models into a single knowledge graph, aiming to bridge the existing gaps in data exchange and enhance the semantic richness of building information models. Through a detailed exploration of existing data exchange mechanisms, popular ontologies, and schema limitations, this research addresses the interoperability dilemma. A practical demonstration, encapsulated in the “LBD-Online-Merge” demo application, validates the methodology's efficacy in real-world scenarios, illustrating its potential to revolutionize building operations management and data preparation for the operational phase of a building's lifecycle. This study significantly contributes to the academic and practical discourse by providing a robust framework for achieving a more integrated, intelligent building information management ecosystem, setting a new benchmark for future research and applications in constructing semantically rich digital twins of the built environment.