Variable Elimination (VE), proposed by Zhang and Poole, is a standard algorithm for performing exact inference in discrete Bayesian networks. VE starts and ends with clear semantics, yet the intermediate factors constructed by VE are seen as not having semantics. In this seminar, we give an algorithm, called Semantics in Inference (SI) that denotes the semantics of every intermediate factor constructed by VE. We show that SI is correct in nearly every instance of Bayesian network inference. SI provides a better understanding of the theoretical foundation of Bayesian networks and can be used for improved clarity, as shown via an examination of Bayesian network literature. This work is also interesting in that it reveals that d-separation plays a much larger role in inference than the literature suggests.