In this chapter we consider how to build a structured knowledge graph (KG) for a geometric and behavioral digital twin in the context of modal testing. The concept is based on combining geometric information from computer-aided design (CAD) model(s) and dynamic properties extracted from modal testing data and a finite element analysis (FEA) to create a digital twin. The material properties of the structure are defined in a separate bill of materials that is uploaded to the digital twin. The functionality of the digital twin is to provide a continuous “digital thread” of events during the modal test and gather the information that at a later stage could be used for validation and updating the finite element model of the structure (although that is not discussed in detail here). The modal testing data is taken from a small-scale three-story structure that is used to demonstrate the concept. The structured KG is built using the Neo4j interface, operated by the py2neo Python package. The KG is defined in an entity-event format that can be dynamically updated as new information is received in the digital twin. The CAD information is integrated into the KG using an STL file format. The KG is seeded with the STL file data and the bill of materials. Then as the modal test proceeds, data segments obtained from the sensors are added to a database and simultaneously added to the KG. As the KG evolves, it creates a digital thread of the test that can be interrogated as required to provide information to the user(s) and enable more effective asset management. The KG can also be integrated into a wider digital twin functionality of the structure.