In decision support systems such as those designed to predict scientific and technical emergence based on analysis of collections of data the presentation of provenance lineage records in the form of a human-readable explanation has been shown to be an effective strategy for assisting users in the interpretation of results. This work focuses on the development of a novel infrastructure for enabling the explanation of hybrid intelligence systems including probabilistic models—in the form of Bayes nets—and the presentation of corresponding evidence. Our design leverages Semantic Web technologies—including a family of ontologies—for representing and explaining emergence forecasting for entity prominence. Our infrastructure design has been driven by two goals: first, to provide technology to support transparency into indicator-based forecasting systems; second, to provide analyst users context-aware mechanisms to drill down into evidence underlying presented indicators. The driving use case for our explanation infrastructure has been a specific analysis system designed to automate the forecasting of trends in science and technology based on collections of published patents and scientific journal articles.