To explain complex phenomena, an explanation system must be
able to select information from a formal representation of domain
knowledge, organize the selected information into multi-sentential
discourse plans, and realize the discourse plans in text. Although
recent years have witnessed significant progress in the development of
sophisticated computational mechanisms for explanation, empirical
results have been limited. This paper reports on a seven year effort
to empirically study explanation generation from semantically rich,
large-scale knowledge bases. In particular, it describes KNIGHT, a
robust explanation system that constructs multi-sentential and
multi-paragrpah explanations from the Biology Knowledge Base, a
large-scale knowledge base in the domain of botanical anatomy,
physiology, and development. We introduce the Two Panel
evaluation methodology and describe how KNIGHT's performance was
assessed with this methodology in the most extensive empirical
evaluation conducted of an explanation system. In this evaluation,
KNIGHT scored within "half a grade" of domain experts, and its
performance exceeded that of one of the domain experts.