Explaining Scientific and Technical Emergence Forecasting

Abstract

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.