The detection and diagnosis of abnormal building behavior is key to further improve the comfort and energy efficiency in buildings. An increasing number of sensors can be utilized for this task but these lead to higher integration effort and the need to capture the sensor interactions. This paper presents a novel diagnostic approach for buildings with complex heating, ventilation, air-conditioning (HVAC) systems. It uses semantic graphs to automatically create the diagnostic model from the building's data points and to identify potential cause-effect-relationships based on past and current time series data. The approach is validated on various simulated examples of a multiroom HVAC control system. The experimental results show that it can diagnose multiple faults with and without delays with high accuracy.