WhyNot: Debugging Failed Queries in Large Knowledge Bases

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

OntoMorph: A Translation System for Symbolic Knowledge

A common problem during the life cycle of knowledge-based systems is that symbolically represented knowledge needs to be translated into some different form. Translation needs occur along a variety of dimensions, such as KR language syntax and expressivity, modeling conventions, representation paradigms, etc. As a tool to support the translation problem, we present the OntoMorph system. OntoMorph provides a powerful rule language to represent complex syntactic transformations, and it is fully integrated with the PowerLoom KR system to allow transformations based on any mixture of syntactic and semantic criteria. We describe OntoMorph's successful application as an input translator for a critiquing system and as the core of a translation service for agent communication. We further motivate how OntoMorph can be used to support knowledge base merging tasks