Abstract
Knowledge representation has been called the most central problem in artificial intelligence, and every system developed in AI has to deal with it at some level or another. Cercone and McCalla (1987) state that “Knowledge Representation is basically the glue that binds much of AI together”; knowledge seems to be a prerequisite for any kind of intelligent activity. All areas of research in AI from game playing to expert systems, from computer vision to natural language processing, require vast amounts of knowledge about the domain within which the system will be operating. Chess programs need to know not only which moves are legal for each piece but also all kinds of heuristics or rules of thumb for deciding the best strategy, and for knowing when the game is, for all intents and purposes, lost or won. Expert systems, of course, are the very embodiment of an expert’s knowledge and experience translated into a series of condition-action rules (when this condition occurs, then take that action). In computer vision it was found that there is too wide a gap between raw image data and any kind of intelligent use of what is ‘seen’. In order to make sense of these images various kinds of knowledge are necessary; for example, knowledge about brightness and brightness change, knowledge about the relation of these changes to texture, edges and surfaces, etc. Natural language processing also requires vast amounts of knowledge; knowledge about the syntax of language, the meaning of words, knowledge about what is assumed as well-known in a conversation and what is implied by a particular choice of words. As we saw in Chapter 2, both literal and metaphoric utterances go beyond the level of the words in the sentences and involve entire semantic domains. Beardsley talks about the connotations of words, proponents of the anomaly view discuss the semantic categories involved in metaphor, and Black claims that words entail entire systems of commonplaces. All of these are a form of knowledge that is necessary for any kind of language comprehension.
The saying that a little knowledge is a dangerous thing is, to my mind, a very dangerous adage. If knowledge is real and genuine, I do not believe that it is other than a very valuable possession, however infinitesimal its quantity may be. Indeed, if a little knowledge is dangerous, where is the man who has so much as to be out of danger?
[T.H. Huxley, On Elemental Instruction in Physiology, 1877]
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Notes
In his article “On the Epistemological Status of Semantic Networks”, Ronald Brachman identifies five levels of primitives for a semantic network system. The levels are: implementational, logical, epistemological, conceputal and linguistic.
The philosophical literature is abundant with research on these different types of reasoning. Some of these we have already discussed, namely, counterfactual, hierarchial, and evidential reasoning. For non-philosophers who wish for more information see, for example, Copi’s Introduction to Logic and Giere’s Understanding Scientific Reasoning.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1991 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Way, E.C. (1991). Knowledge Representation. In: Knowledge Representation and Metaphor. Studies in Cognitive Systems, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7941-4_3
Download citation
DOI: https://doi.org/10.1007/978-94-015-7941-4_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4079-4
Online ISBN: 978-94-015-7941-4
eBook Packages: Springer Book Archive