When a query to a knowledge-based system fails and returns "unknown", users are confronted with a problem: Is relevant knowledge missing or incorrect? Is there a problem with the inference engine? Was the query ill-conceived? Finding the culprit in a large and complex knowledge base can be a hard and laborious task for knowledge engineers and might be impossible for non-expert users. To support such situations we developed a new tool called "WhyNot" as part of the PowerLoom knowledge representation and reasoning system. To debug a failed query, WhyNot tries to generate a small set of plausible partial proofs that can guide the user to what knowledge might have been missing, or where the system might have failed to make a relevant inference. A first version of the system has been deployed to help debug queries to a version of the Cyc knowledge base containing over 1,000,000 facts and over 35,000 rules.
To appear in Proceedings of the Fifteenth Innovative Applications of Artificial Intelligence Conference, pp. ?? 2002.The full paper is available in PDF. Get PDF. (8pp)