Research in Natural Language Processing

s of Recent Technical Reports INTERACTIVE CLASSIFICATION A Technique for the Aquisition and Maintenance of Knowledge Bases, Tim Flnln and David Silverman, MS-CIS-84-17. The practical application of flame-based knowledge-based systems, such as in expert systems, requires the maintenance of potentially very large amounts of declarative knowledge stored in their knowledge bases (KBs). As a KB grows in size and complexity, it becomes more difficult to maintain and extend. Even someone who is familiar with the representation and the contents of the existing KB may introduce inconsistencies and errors whenever an addition or modification is made. This paper describes an approach to thisproblem based on a tool called an interactive classifier. An interactive classifier uses the contents of the existing KB and knowledge about its representation to assist the person who is maintaining the KB in describing new KB objects. The interactive classifier will identify the ~ppmpriate taxonomic location for the newly described object and add it to the KB. The new object is allowed to be a generalization of existing KB objects, enabling the system to learn more about existing objects. The ideas have been tested in a system call KuBIC, for Knowledge Base Interactive Classifier, and are being extended to a more complete knowledge representation language. Correcting Object-Related Misconceptions: How Should The System Respond?, Kathleen F. McCoy, MS-CIS-84-1& This paper describes a computational method for correcting users' misconceptions concoming the objects modeled by a computer system. The method involves classifying object-related nnsconceptions according to the knowledge-base feature involved in..the incorrect information. For each resulting class sub-types are identified, according to the structure of the knowledge base, wmcn indicate what information may be supporting the misconception and, therefo.re, what informatio 9 to tnelufle .m the rysponse. Such a characterization, along with a model of what the user knows, enables IRe system to reason m a aomam-moepenoent way about how best to correct the user.