A Unified Framework for Intelligent Product Configuration Using Knowledge Graphs and Bayesian Networks

The increasing complexity of product configurations demands intelligent systems that effectively integrate customer requirements, dependencies, and uncertainties. This paper introduces a unified framework combining Knowledge Graphs (KGs) and Bayesian Networks (BNs) to enhance the efficiency and adaptability of product configuration processes. KGs provide a semantic foundation for product information, ensuring interoperability and explicit relationship modeling. BNs enhance this through probabilistic reasoning, allowing the system to manage uncertainties and dynamically generate optimal configurations. The integration of deterministic, rule-based reasoning from ontologies with the probabilistic nature of BNs automates suggestions, predicts user preferences, and reduces complexity. This framework streamlines user interactions through intelligent form pre-filling and contextually relevant suggestions, even under uncertainty. By employing an ontology-based representation of BNs, the components fit seamlessly into the KG, creating a cohesive and unified framework that balances scalability and user-centric design to address modern configuration challenges.