With improvements in sensor technology, sensor networks, and data storage, building automation now incorporates a large number of data points into every element of building systems. Varying naming conventions and schemas assigned to these elements by different companies and field engineers pose a challenge to identifying relationships between building systems. To solve this issue, we developed a framework for the Building Automation and Control Network (BACnet) data point to establish the physical relationships between building elements. Specifically, this research investigated the relationship between air handling units (AHU) and variable air volume (VAV) terminal units. The framework mainly consists of two methods. Firstly, filtering and Random Forest classification techniques identify the semantic information of data points. This method classifies supply air duct pressure (SADP) data points in AHU and damper position (DP) data points in VAV terminal units with 94.9 percent accuracy. The second method calculates the absolute cross correlations between classified data points, and then associates the relationship by cross correlation results of nine-month profiles. This automated association method results in 79.9 percent accuracy. The suggested framework will help users find AHU-VAV relationships, which could be challenging due to a large number of heterogeneous sensors and sensor networks, and inconsistent and erratic sensor nomenclature in the modern building.