The complexity of modern construction projects necessitates collaboration among diverse stakeholders and the handling of substantial data volumes. Significant investments in digitization have occurred globally to address this complexity, emphasising the need for interoperability among standards and diverse knowledge sources. The emergence of Digital Building Twins (DBTs) further underscores the importance of integrating heterogeneous data to create comprehensive digital representations of buildings. DBTs enable real-time data integration and support various phases of architectural development, offering practitioners access to historical, present, and predictive data. In this context, our research focuses on enhancing the accessibility and interpretability of DBT data through natural language querying. Leveraging domain-specific ontology and advanced AI techniques, our approach facilitates efficient communication between users and DBTs, enabling rapid extraction of specific building details. This paper presents our methodology, including knowledge representation, semantic analysis, and information extraction, along with evaluation results demonstrating its effectiveness in improving DBT querying performance.