Abstract

Even quite sophisticated users can experience difficulty navigating large collections of data to locate the answers to their queries. We describe AskCal, a system that employs natural language processing, an ontology, a query planner, and various feedback mechanisms to assist a user in refining his or her query and then in executing and visualizing it. Tracing several interactions with AskCal in the domain of energy time series, we show how a combination of modalities, including ATN parsing of free-form natural language questions, user modification of predefined template queries, and fall-back parsing by picking out landmark terms, support a wide variety of user queries while reducing user query formulation effort. We illustrate the use of feedback mechanisms to guide the user toward regions of the query space where useful data can be found.

Date

May 19, 2002

Authors

Andrew Philpot, Jose Luis Ambite, Eduard Hovy

Journal

ACM International Conference Proceeding Series

Volume

129

Pages

1-7