Bayesian network (BN) has been a popular tool for gaining mechanistic understanding of variables by revealing how the variables influence each other. It has been found very effective in a few studies in quality control and process monitoring. However, for complex problems where the structure of a BN is unknown, a common approach is to learn the BN structure from observational data. A fundamental bottleneck of this approach is that observational data can only be used to discover part of the influential relationships among variables. To overcome this problem, we propose to combine observational data and expert knowledge. To the best of the author's knowledge, our approach is the first of its kind that formulates an experimental design framework to automate the expert elicitation process and collect the most informative expert knowledge, optimally matched to the observational data, to learn the BN structure. Note to Practitioners - As design of experiment techniques have been widely used in practice to automate the systematic data collection and experimental procedures by perturbing the system in order to better understand the relationships between the factors, here, we provide practitioners a counterpart approach for systematic expert knowledge elicitation. It can be used in combination with a imperfect data set, while the data could provide a starting point and further inform the expert knowledge elicitation by prioritizing the information queries. The data and expert knowledge gathered will be fused to learn the relationships between the factors with the estimation of the uncertainties.