This work focuses on the interoperability of systems for creating Digital Twins (DTs), focusing on the complex challenges of uniting data from hetero geneous systems that use diverse API paradigms and data structures. Digital Twins, as digital replicas of real-world systems. For that reason, their devel opment poses significant challenges, particularly in systems of systems (SoS) architectures, where data originates from numerous, often heterogeneous and incompatible sources. This work proposes a method to integrate systems to achieve what was done: a thorough literature review evaluates the state-of-the-art approaches, two prototypes implemented using GraphQL, and three basic performance tests to evaluate the benefits and limitations of the proposed approach. The first prototype employs a fixed integrations approach, while the second lever ages an automated system composition algorithm using a JSON description file. The solution is designed to address challenges such as varying data structures, taxonomy differences, and different paradigms, like REST, gRPC, SOAP, etc, all of which traditionally complicate system integration. By employing GraphQL, the study achieves a more flexible data integration mechanism that can handle different paradigms, enabling seamless querying and aggregation across systems. The research concludes by discussing potential optimizations and future directions, such as incorporating other system descriptions like knowledge graphs, ontologies or specific languages for enhanced data modelling and improving query composition techniques.