The integration of data capture, analysis, monitoring, and control technologies is rapidly becoming the cornerstone of next-generation smart buildings. However, developing digital twins that dynamically interact with these buildings presents a significant challenge. In this paper, we study the most appropriate data models for leveraging a digital twin from data-driven smart buildings. We propose a framework that exploits a knowledge graph to directly address the challenges encountered in real-world building management systems, ensuring that the information is comprehensible as a preliminary step to intelligent decision-making. Furthermore, we validate this proposal for improving building performance and sustainability through a real-world use case. The experimental results, utilizing dynamic data streams from the Internet of Things (IoT), demonstrate promising outcomes. This research paves the way for using graph-based models and algorithms as digital twin enhancers for managing data-driven smart buildings.