Directed probabilistic graphs capture essential qualitative properties of a domain, along with its causal structure. Concepts such as relevance and conflicting evidence have a natural, formally sound meaning in probabilistic models. Probabilistic schemes support sound reasoning at a variety of levels ranging from purely quantitative to purely qualitative levels. Probabilistic knowledge representations provide insight into the foundations of logic-based schemes for reasoning under uncertainty, showing their difficulties in highly uncertain domains. Finally, probabilistic knowledge representations support automatic generation of understandable explanations of inference for the sake of user interfaces to intelligent systems.