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• W72765941 abstract "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 multisentential discourse plans, and realize the discourse plans in text. Although recellit 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.We first describe Knight, a robust explanation system that constructs multi-sentential and multi-paragraph explanations from the Biology Knowledge Base, a large-scale knowledge base in the domain of botanical anatomy, physiology, and development. We then introduce the Two Panel evaluation methodology and describe how Knight's performance was assessed with this methodology in the most extensive empirical evaluation conducted on 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." @default.
• W72765941 created "2016-06-24" @default.
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• W72765941 date "1996-08-04" @default.
• W72765941 modified "2023-09-24" @default.
• W72765941 title "Scaling up explanation generation: large-scale knowledge bases and empirical studies" @default.
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