WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information

We present WatsonPathsTM , a novel system that can answer scenario-based questions, for example medical questions that present a patient summary and ask for the most likely diagnosis or most appropriate treatment. WatsonPaths builds on the IBM WatsonTM question answering system that takes natural language questions as input and produces precise answers along with accurate confidences as output. Watson-Paths breaks down the input scenario into individual pieces of information, asks relevant subquestions of Watson to conclude new information, and represents these results in a graphical model. Probabilistic inference is performed over the graph to conclude the answer. On a set of medical test preparation questions, Watson-Paths shows a significant improvement in accuracy over the base Watson QA system. We also describe how WatsonPaths can be used in a collaborative application to help users reason about complex scenarios.

By: Adam Lally, Sugato Bachi, Michael A. Barborak, David W. Buchanan, Jennifer Chu-Carroll, David A. Ferrucci*, Michael R. Glass, Aditya Kalyanpur, Erik T. Mueller, J. William Murdock, Siddharth Patwardhan, John M. Prager, Christopher A. Welty

Published in: RC25489 in 2014


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