CSE 663: ADVANCED TOPICS IN KNOWLEDGE REPRESENTATION

Prof. William J. Rapaport

Spring 2002
TTh 9:30-10:50 a.m., 127A Cooke, Reg. #475069
Prerequisites: CSE 4/572 or CSE 676 or permission of instructor

Official Catalog Description:

A second graduate course in knowledge representation and reasoning covering such topics as automated theorem proving, semantic network implementation, etc., and surveying knowledge representation and reasoning topics not covered in other graduate-level courses. Topics will vary according to instructor and student interests.

Actual Description for the Spring 2002 Version:

This will be a practical course in applying knowledge-representation and reasoning techniques to the solution of two current research projects.

  1. Prof. William J. Rapaport (Department of Computer Science and Engineering) and Prof. Michael W. Kibby (Department of Learning and Instruction and Center for Literacy and Reading Instruction) are principal investigators on a National Science Foundation grant, under the Research on Learning and Education (ROLE) program:

    CONTEXTUAL VOCABULARY ACQUISITION:
    Development of a Computational Theory and Educational Curriculum

    We are developing a computational theory of how natural-language-understanding systems can automatically acquire new vocabulary by determining from context the meaning of words that are unknown, misunderstood, or used in a new sense.

    We propose:

    (a) to extend and develop algorithms for computational contextual vocabulary acquisition (CVA): learning, from context, meanings for "hard" word: nouns (including proper nouns), verbs, adjectives, and adverbs,

    (b) to unify a disparate literature on the topic of CVA from psychology, first- and second-language (L1 and L2) acquisition, and reading science, in order to help develop these algorithms, and

    (c) to use the knowledge gained from the computational CVA system to build and to evaluate the effectiveness of an educational curriculum for enhancing students' abilities to use deliberate (i.e., non-incidental) CVA strategies in their reading of science, math, engineering, and technology texts at the middle-school and college undergraduate levels: teaching methods and guides, materials for teaching and practice, and evaluation instruments.

    The knowledge gained from case studies of students using our CVA techniques will feed back into further development of our computational theory.

    The work involves using natural-language-processing techniques, such as ATN (augmented-transition-network) grammars, and the SNePS knowledge representation and reasoning system.

    For more information on the project, see:

    Further information on SNePS can be found at the SNePS homepage.

    Further information on ATNs can be found in the SNePS Manual.

  2. Prof. William J. Rapaport and Prof. Stuart C. Shapiro (Department of Computer Science and Engineering) and Dr. Peter Winkelstein (Department of Pediatrics and Children's Hospital) are investigating the use of the SNePS knowledge representation and reasoning system, with its natural-language-processing capabilities, for reading, understanding, and making recommendations about hospital discharge summaries.

    The work involves using natural-language-processing techniques such as ATN (augmented-transition-network) grammars, and/or the LKB system, and/or information extraction systems (e.g., TextPro) to parse hospital discharge summaries into a SNePS representation for further processing by SNePS's reasoning facilities, possibly with an eye towards "translating" the discharge summary (intended as a means of communication between medical professionals) into plain English for lay persons (e.g., patients and their families) to be able to understand.

    Further information on the project can be found at: http://www.cse.buffalo.edu/~rapaport/mednlp.ps.

    Copyright © 2001 by William J. Rapaport (rapaport@cse.buffalo.edu)
    file: flyer.663.S01.29oc01.html