Interactive classification: A technique for acquiring and maintaining knowledge bases

The practical application of knowledge-based systems, such as in expert systems, often requires the maintenance of large amounts of declarative knowledge. As a knowledge base (KB) grows in size and complexity, it becomes more difficult to maintain and extend. Even someone who is familiar with the knowledge domain, how it is represented in the KB, and the actual contents of the current KB may have severe difficulties in updating it. Even if the difficulties can be tolerated, there is a very real danger that inconsistencies and errors may be introduced into the KB through the modification. This paper describes an approach to this problem based on a tool called an interactive classifier. An interactive classifier uses the contents of the existing KB and knowledge about its representation to help the maintainer describe new KB objects. The interactive classifier will identify the appropriate 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.

[1]  Janice Sue Aikins,et al.  Prototypes and production rules : a knowledge representation for computer consultations , 1980 .

[2]  Reid G. Smith,et al.  IMPULSE: A Display Oriented Editor for STROBE , 1983, AAAI.

[3]  James G. Schmolze,et al.  Classification in the KL-ONE Knowledge Representation System , 1983, IJCAI.

[4]  Mark S. Fox,et al.  Reasoning With Incomplete Knowledge in a Resource-Limited Environment: Integrating Reasoning and Knowledge Acquisition , 1981, IJCAI.

[5]  Ronald J. Brachman,et al.  An Overview of the KL-ONE Knowledge Representation System , 1985, Cogn. Sci..

[6]  Elaine Rich,et al.  User Modeling via Stereotypes , 1998, Cogn. Sci..

[7]  Bonnie Webber,et al.  In response: next steps in natural language interaction , 1984 .

[8]  John P. McDermott,et al.  R1: A Rule-Based Configurer of Computer Systems , 1982, Artif. Intell..

[9]  Drew McDermott,et al.  Non-Monotonic Logic I , 1987, Artif. Intell..

[10]  Timothy W. Finin,et al.  GUMS₁ : A General User Modeling System , 1986, HLT.

[11]  Ronald J. Brachman,et al.  An overview of the KL-ONE Knowledge Representation System , 1985 .

[12]  Timothy W. Finin,et al.  Natural language interactions with artificial experts , 1986, Proceedings of the IEEE.

[13]  Hassan Aït-Kaci.  Type Subsumption as a Model of Computation , 1984, Expert Database Workshop.

[14]  E. Shortliffe.  Computer-based medical consultations: mycin (elsevier north holland , 1976 .

[15]  Ronald J. Brachman,et al.  A Structural Paradigm for Representing Knowledge. , 1978 .

[16]  Ruven Brooks,et al.  Controlling Question Asking in a Medical Expert System , 1979, IJCAI.

[17]  William A Woods.  Theoretical Studies in Natural Language Understanding. , 1980 .

[18]  Ethan A. Scarl,et al.  Interactive Frame Instantiation , 1980, AAAI.

[19]  Mark S. Fox,et al.  Experiences with SRL: An Analysis of a Frame-based Knowledge Representation , 1984, Expert Database Workshop.

[20]  Hector J. Levesque,et al.  Krypton: A Functional Approach to Knowledge Representation , 1983, Computer.

[21]  Francisco Corella.  Semantic Retrieval and Levels of Abstraction , 1984, Expert Database Workshop.

[22]  John Gaschnig,et al.  MODEL DESIGN IN THE PROSPECTOR CONSULTANT SYSTEM FOR MINERAL EXPLORATION , 1981 .