As service-oriented environments grow in size and complexity, managing their performance becomes increasingly difficult. To assist administrators, autonomic techniques have been adopted to permit these environments to be self-managing (problem localization, workload management, etc.). These techniques need a sense of system state and the ability to project a new state given some change within the environment. Recent work addressing this issue frequently used statistically learned models which were derived entirely from data. However, many environments already have management facilities in place that could provide precise and useful insights (e.g. workflows) into the system. This paper introduces a method of modeling service-oriented system performance using Bayesian networks and specifically addresses the benefits obtained by incorporating these insights into the model learning process. To further minimize model building costs, we devise a decentralized method to concurrently learn parts of the model where knowledge inclusion is impossible. Simulations and applications in actual environments show significant reductions in learning time, better accuracy and stronger tolerance to small learning data sets.