Natural Language Question Answering and Analytics for Diverse and Interlinked Datasets

Dezhao Song, Frank Schilder, Charese Smiley, Chris Brew

Abstract: Previous systems for natural language questions over complex linked datasets require the user to enter a complete and well-formed question, and present the answers as raw lists of entities. Using a feature-based grammar with a full formal semantics, we have developed a system that is able to support rich autosuggest, and to deliver dynamically generated analytics for each result that it returns.