Serving as exploratory data analysis tools, Bayesian networks (BNs) can be automatically learned from data to compactly model direct dependency relationships among the variables in a domain. A major challenge in BN learning is to effectively represent and incorporate domain knowledge in the learning process to improve its efficiency and accuracy. In this paper, we examine two types of domain knowledge representation in BNs: matrix and rule. We develop a set of consistency checking mechanisms for the representations and describe their applications in BN learning. Empirical results from the canonical Asia network example show that topological constraints, especially those imposed on the undirected links in the corresponding completed partially directed acyclic graph (CPDAG) of the learned BN, are particularly useful. Preliminary experiments on a real-life coronary artery disease dataset show that both efficiency and accuracy can be improved with the proposed methodology. The bootstrap approach adopted in the BN learning process with topological constraints also highlights the set of the learned links with high significance, which can in turn prompt further exploration of the actual relationships involved.