An Interactive Visual Language for Term Subsumption Languages

A visual language is defined equivalent in expressive power to term subsumption languages expressed in textual form. To each knowledge representation primitive there corresponds a visual form expressing it concisely and completely. The visual language and textual languages are intertranslatable. Expressions in the language are graphs of labeled nodes and directed or undirected arcs. The nodes are labeled textually or iconically and their types are denoted by six different outlines. Computer-readable expressions in the language may be created through a structure editor that ensures that syntactic constraints are obeyed. The editor exports knowledge structures to a knowledge representation server computing subsumption and recognition, and maintaining a hybrid knowledge base of concept definitions and individual assertions. The server can respond to queries graphically displaying the results in the visual language in editable form. Knowledge structures can be entered directly in the editor or imported from knowledge acquisition tools such as those supporting repertory grid elicitation and empirical induction. Knowledge structures can be -exported to a range of knowledge-based systems.

[1]  Scott E. Fahlman,et al.  Thesis progress report : a system for representing and using real-world knowledge , 1975 .

[2]  David J. Israel,et al.  Research in Knowledge Representation for Natural Language Understanding , 1980 .

[3]  Gary Rawlinson,et al.  Applications of Expert Systems, Volume 2 , 1990 .

[4]  Brian R. Gaines,et al.  Knowledge Acquisition Tools for Expert Systems , 1988 .

[5]  Brian R. Gaines,et al.  Eliciting Knowledge and Transferring It Effectively to a Knowledge-Based System , 1993, IEEE Trans. Knowl. Data Eng..

[6]  OÙ EN SOMMES-NOUS Roth , 1961 .

[7]  Ephraim P. Glinert,et al.  Visual Programming Environments: Paradigms and Systems , 1990 .

[8]  Hector J. Levesque,et al.  An Essential Hybrid Reasoning System: Knowledge and Symbol Level Accounts of KRYPTON , 1985, IJCAI.

[9]  Lenhart K. Schubert,et al.  Toward a State Based Conceptual Representation , 1975, IJCAI.

[10]  Deborah L. McGuinness,et al.  CLASSIC: a structural data model for objects , 1989, SIGMOD '89.

[11]  Ronald J. Brachman,et al.  ON THE EPISTEMOLOGICAL STATUS OF SEMANTIC NETWORKS , 1979 .

[12]  Mark H. Burstein,et al.  The KREME Knowledge Editing Environment , 1987, Int. J. Man Mach. Stud..

[13]  Scott E. Fahlman,et al.  NETL: A System for Representing and Using Real-World Knowledge , 1979, CL.

[14]  Brian R. Gaines Integrating Rules in Term Subsumption Knowledge Representation Servers , 1991, AAAI.

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

[16]  Nicholas V. Findler,et al.  Associative Networks- Representation and Use of Knowledge by Computers , 1980, CL.

[17]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[18]  Hiroyuki Watanabe,et al.  Heuristic Graph Displayer for G-BASE , 1989, Int. J. Man Mach. Stud..

[19]  Jock D. Mackinlay,et al.  Expressiveness of Languages , 1984, AAAI.

[20]  D. Bobrow,et al.  Representation and Understanding: Studies in Cognitive Science , 1975 .

[21]  John T. Nosek,et al.  A Comparison of Formal Knowledge Representation Schemes as Communication Tools: Predicate Logic vs Semantic Network , 1990, Int. J. Man Mach. Stud..

[22]  Ronald J. Brachman,et al.  What's in a Concept: Structural Foundations for Semantic Networks , 1977, Int. J. Man Mach. Stud..

[23]  Brian R. Gaines Empirical investigation of knowledge representation servers: design issues and applications experience with KRS , 1991, SGAR.

[24]  B. Woodward Knowledge acquisition at the front end: defining the domain , 1990 .

[25]  William A. Woods,et al.  What's in a Link: Foundations for Semantic Networks , 1975 .