Complex Internet of Things (IoT) environments present significant challenges in device discovery and platform configuration, especially as the number of interconnected devices continues to grow. This research addresses these challenges by leveraging the complementary strengths of symbolic and connectionist artificial intelligence (AI) within a neuro-symbolic system. We propose a comprehensive approach to IoT platform configuration that integrates neuro-symbolic reasoning and conceptual modelling techniques, enhancing both efficiency and explainability. Following the process model of design science research, our work introduces two key artifacts: the Instantiator Pipeline and the IoT2Model method. The Instantiator Pipeline automates the discovery and integration of IoT devices, minimising manual intervention and ensuring seamless interoperability. During the initial iteration of the applied research methodology, it was recognised that effective IoT platform configuration must extend beyond device registration to include the creation of scenario-specific rules. To address this, the IoT2Model method was developed, enabling users to define models that represent these rules, thereby tailoring the behaviour of their IoT deployments to meet specific requirements. By utilising existing open technologies, our solution ensures flexibility and adaptability, overcoming the limitations of related works. The proposed neuro-symbolic AI system not only streamlines the configuration process but also provides a robust, scalable and explainable solution for managing complex IoT environments, paving the way for future advancements of intelligent information systems.

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