The precise integration of drivetrain components in SCARA robots, including sensors, controllers, motor drivers, motors, and gears, is crucial for ensuring system functionality. Selecting appropriate components requires interdisciplinary interface compatibility, traditionally managed through manual selection and expert knowledge. However, this process is often inefficient and error-prone, particularly when integrating components from different vendors. To support human engineers, this paper presents an ontology-based approach leveraging OWL and the Semantic Web Rule Language (SWRL). The method automates the identification and pairing of compatible component interfaces across mechanical, electrical, and software domains by defining SWRL rules. These rules allow an ontology reasoner to infer and derive matching properties, ensuring logical consistency and accuracy. The proposed method not only simplifies the seamless pairing of components but also supports dynamic updates, enabling the integration of newly added components with minimal manual intervention. The evaluation involves creating 270 artificial component instances with diverse interface properties, validating the SWRL rules, and retrieving compatible component sets via SPARQL queries. This automated workflow significantly enhances the efficiency, accuracy, and flexibility of configuring robot drivetrain systems. The results demonstrate marked improvements in system design, scalability, and the ability to reuse pairing knowledge for future applications.