This paper describes the development of a novel data model for storing and sharing data obtained from engine experiments, it then outlines a methodology for automatic model development and applies it to a state-of-the-art engine combustion model (including chemical kinetics) to reduce corresponding model parameter uncertainties with respect engine experiments. These challenges are met by adopting the latest developments in the semantic web to create a shared data model resource for the IC engine development community. Application models can then access this database to automatically set-up simulations and validation exercises. A methodology for incorporating experimental and model uncertainties into model optimization and final results for multi-parameter and complex modeling applications is presented. Data from seven operating points have been extracted from the proposed data model and have been incorporated into a state-of-the-art in-cylinder IC engine model through the optimization of forty-two model parameters whilst accounting for the model parameter and experimental uncertainties.