Document Type
Thesis
Date of Award
5-31-1993
Degree Name
Master of Science in Computer Science - (M.S.)
Department
Computer and Information Science
First Advisor
Yehoshua Perl
Second Advisor
Jason T. L. Wang
Third Advisor
Daniel Y. Chao
Abstract
Applying Object-oriented concepts to the design of complex graphical interface has received great attention in the database and knowledge representation disciplines. Traditional CAD systems can not support efficient environments for design processes because they store information about all the objects for display purposes but do not store any knowledge for reasoning purposes. They are called "knowledge poor". "Graphical Deep Knowledge" in Artificial Intelligence has been proven successful to represent knowledge about objects for display purposes as well as reasoning purposes. We introduwd the theory of "Graphical Deep Knowledge" into the object-oriented database s stem VML to design a "Knowledge rich" system which can support better graphical interface. We showed that the theory of "Graphical Deep Knowledge" can improve graphical interface design and functions as a flexible, and knowledgeable tool for design processes. Based on the presented theory, we developed a system called GDKRIVVOOD (Graphical Deep Knowledge Representation In Vodak/VML Object-Oriented Database) for circuit board design.
Recommended Citation
Wang, Jue, "Graphical deep knowledge representation in VODAK/VML object-oriented database" (1993). Theses. 2197.
https://digitalcommons.njit.edu/theses/2197
