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Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions. However, existing research on PCs primarily focuses on data-driven parameter learning, with limited exploration of knowledge-intensive learning and structure learning. In this work, we propose to address these gaps by introducing a comprehensive approach to incorporating various kinds of domain knowledge into the learning of a PC's structure as well as its parameters.