Predictive analytic tasks identify likely future outcomes based on historical and current data. Predictions alone, however, are usually insufficient for users to make sound decisions, due to reasons such as regulatory requirements, distrust towards black box technology. To this end, explanations have been used as one way to enable adoption of predictive analytics. In particular, semantics-rich explanations that leverage knowledge graphs are explored by both academics and practitioners. This chapter presents three case studies: predictive analytics for identifying abnormal expense claims, mitigating project risks, and predicting pronunciations and learning the language model of Chinese characters. In addition to predictions, explanations play an important part in these cases. They could impact decision making, e.g., by showing that a project is risky likely because of the incompetent delivery centers, or they can enhance users’ trust in the predictive models, e.g., by presenting the dependencies exist between the pronunciation of a Chinese character and that of its substructures. Regardless of the size or the form of the knowledge graphs, the three case studies show that explanations built on domain knowledge add invaluable insights to predictive analytics.