Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-09T20:44:37.186Z Has data issue: false hasContentIssue false
  • English
  • Français

Pictorial knowledge representation using pictorial semantic networks

Published online by Cambridge University Press:  09 March 2009

Edward T. Lee
Edward T. Lee
Affiliation:
Department of Electrical & Computer Engineering, University of Miami, Coral Gables, Florida 33142 (USA)
Get access Rights & Permissions [Opens in a new window]

Summary

Classifications of pictures and pictorial knowledge are presented. Pictorial knowledge is divided into three classes – angular pictorial knowledge, side pictorial knowledge, and angular and side pictorial knowledge. A block diagram of these three pictorial knowledge classes and a pictorial knowledge transformation module is also presented with illustrative examples. Pictorial semantic networks which in terms of pictorial nodes, property nodes, “is a” links, “has property” links, and “if and only if” links are introduced. Transitivity, generalization, specialization, inheritance hierarchy, and knowledge transformation properties are stated and illustrated by examples. Triangular, quadrangular, and polygonal knowledge representation using pictorial semantic networks are presented. The concepts of deducible property nodes are also presented with illustrative examples. Additional facts can be established from pictorial semantic networks. Thus, pictorial semantic networks are a useful way to represent pictorial knowledge in domains that use well-established taxonomies to simplify problem solving in pictorial information systems. Pictorial semantic networks offer what appears to be a fertile field for future study. The results may have useful applications in knowledge representation, expert systems, artificial intelligence, knowledge - based systems, pictorial information systems and related areas.

Keywords

SemanticNetworkPictures
Type
Research Article
Information
Robotica , Volume 6 , Issue 2 , April 1988 , pp. 155 - 160
Copyright
Copyright © Cambridge University Press 1988

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Quillian, M.R., Semantic Memory, Report AFCRL-66–189, Bolt Beranek & Newman, Cambridge, Massachusetts (1966).CrossRefGoogle Scholar
2.Quillian, M.R. “Word concepts: A theory and simulation of some basic semantic capabilities” Behavioral Science, 12, No. 5, 410–430 (1967).CrossRefGoogle ScholarPubMed
3.Quillian, M.R., “Semantic memory” In: Semantic Information Processing (Minsky, M., Ed.) (MIT Press, Cambridge, Massachusetts, 1968), pp. 227–270.Google Scholar
4.Quillian, M.R., “The teachable language comprehender: A simulation program and theory of language” Commun. ACM 12, No. 8, 459–476 (08, 1969).CrossRefGoogle Scholar
5.Bell, A. and Quillian, M.R., “Capturing concepts in a semantic net” In: Associative Information Techniques (Jacks, E.L., Ed.) (Am. Elsevier, New York, 1971) pp. 3–25.Google Scholar
6.Bobrow, D.G. and Collins, A.M. (Eds.) Representation and Understanding: Studies in Cognitive Science (Academic Press, New York, 1975).Google Scholar
7.Findler, N.V. (Ed.) Associative Networks: Representation and Use of Knowledge by Computers (Academic Press, New York, 1979).Google Scholar
8.Haugeland, J. (Ed.) Mind Design: Philosophy, Psychology, Artificial Intelligence. (Bradford Books, Vermont, 1981).Google Scholar
9.Association for Computing Machinery Communications of the ACM. Special Issue on Knowledge Representation 26, 10 [10, 1983].Google Scholar
10.Association for Computing Machinery Communications of the ACM, Special Issue on Architectures for Knowledge-Based Systems 28, 9 [09, 1985].Google Scholar
11.Waterman, D.A., A Guide to Expert Systems (Addison-Wesley New York, 1986).Google Scholar
12.Lee, E.T., “Proximity measures for the classification of geometric figures” J. Cybernetics, 2, 443–59 (1972).Google Scholar
13.Pavlidis, T., Structural Pattern Recognition (Springer-Verlag, Berlin, 1977).CrossRefGoogle Scholar
14.Lee, E.T. and Chu, P., Two dimensional grammars for generating squares and rhombus. (Kybernetes 16, No. 2, 121–123 (1987)).CrossRefGoogle Scholar
15.Bachman, R.J. and Smith, B.C., “Special Issue on Knowledge Representation” SIGART 70 (1980).Google Scholar
16.Barr, A. and Feigenbaum, E.A., The Handbook of Artificial Intelligence, Vols. 1, 2 and 3 (Kaufmann, Los Altos, 1982).Google Scholar
17.Feigenbaum, E.A. and McCorduck, P., The Fifth Generation (Addison-Wesley, Reading, Mass., 1983).Google Scholar
18.Kulikowski, C.A., “AI methods and systems for medical consultation” IEEE Trans. Pattern Anal. Machine Intelligence 464–476 (1980).CrossRefGoogle Scholar
19.Rich, E., Artificial Intelligence (McGraw-Hill, New York, 1983).Google Scholar
20.Shortliffe, E.H., Computer-Based Medical Consultations: MYCIN (American Elsevier, New York, 1976).Google Scholar
21.Weiss, S.M. et al. , “A guide to the use of the EXPERT consultation system” Tech. Rep. CBM-TR-94 (C.S. Dept., Rutgerst University, New Brunswick, N.J., 1980).Google Scholar
22.Duda, R.O. and Shortliffe, E.H., “Expert systems research” Science 220, 261–268 (1983).CrossRefGoogle ScholarPubMed
23.Nau, D., “Expert computer systems” IEEE computer 16, 63–85 (1983).CrossRefGoogle Scholar
24.Association for Computing Machinery, Communications of the ACM, Special Section on Robotics 29, 6 [06, 1986].Google Scholar
25.Zadeh, L.A., “Test-score semantics for natural languages and meaning-representation via PRUF” In: Empirical Semantics (Rieger, B.B., Ed.) (Brockmeyer, Bochum, 1981) pp. 281–349.Google Scholar
26.Davis, R. and Lenat, O.B., Knowledge-Based Systems in Artificial Intellience (McGraw-Hill, New York, 1982).Google Scholar
27.Lee, E.T. “Simularity retrieval for pictorial databases” In: Management and Office Information System (Chang, S.K., Ed.) (Plenum, New York, 1984) pp. 253–288.CrossRefGoogle Scholar
28.Lee, E.T. “Application of the entity-relationship approach to similarity-driven pictorial database design” In: Proc. of 4th ER Conference,Chicago, Illinois. 18–21 (1984).Google Scholar
29.Bracman, R.J., “On the epistemological status of semantic networks” In: Associative Networks: Representation and Use of Knowledge by Computers (Findler, N.V., Ed.) (Academic Press, New York, 1979) pp. 3–50.CrossRefGoogle Scholar
30.Norman, D.A. and Rumelhart, D.E. (Eds.), Explorations in Cognition (W.H. Freeman and Company, San Francisco 1975).Google Scholar
31.Lee, E.T., “Similarity retrieval techniques” In: Pictorial Information Systems (Chang, S.K. and Fu, K.S., Eds.) (Springer-Verlag, Berlin, 1980) pp. 128–176.CrossRefGoogle Scholar
32.Lee, E.T., “Shape-oriented storage and retrieval of geometric figures and chromosome images” Information Processing and Management 12, No. 1, 95–114 (1976).CrossRefGoogle Scholar
33.Chang, S.K. and Fu, K.S., (Eds.), Pictorial Information Systems (Springer-Verlag, Berlin, 1980).CrossRefGoogle Scholar
34.Lee, E.T., “Space object surveillance and identification” Policy Analysis and Information Systems, 3, NO. 1, 171–185 (1979).Google Scholar
35.Lee, E.T., Chu, P. and Wu, C. Peng, “Application of entity-relationship model to picture description” (to appear in Robotica).Google Scholar