A knowledge representation selection criteria

Neena Wadhwa

Date of Publication

1989

Document Type

Master's Thesis

Degree Name

Master of Science in Teaching in Computer Science

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Nopporn Luangprasert

Defense Panel Chair

Benedict Fernandes

Defense Panel Member

Jopillo, Marilou Jopillo
Kang Mun Arturo Tan

Abstract/Summary

Work in AI concerns the nature and functioning of knowledge wherein flexible representations are required in order to understand and generate expert behavior. Growing interest in generating expert behavior through the use of expert system/decision support system technology raises the issue on the importance of knowledge representation (KR) for this technology as several existing knowledge representation schemes affect and effect inference in significantly different ways. As a result, knowledge engineer (KE) is, often, failed to come up with a correct representational framework to formulate knowledge in a way the system is expected to behave. Furthermore, the unavailability of a relevant criteria for a good knowledge representation scheme itself has been a basic and big barrier in their work. Thus, this study focuses on: 1) properties of consistent knowledge representation framework and designs a criteria set 2) analysis and evaluation of existing KR schemes on the basis of designed criteria set thereby, giving aid to KE to select ideal KR scheme based on a relevant criteria. This study can be extended with the implementation of the KR schemes and can be evaluated according to the designed criteria set to obtain performance measurements for different problem domains.

Abstract Format

html

Language

English

Format

Print

Accession Number

TG01624

Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

[154] p., 28 cm.

Keywords

Knowledge representation (Information theory)

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