Page 249 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Unit 13: Expert System Architecture

13.4 Semantic Memory Notes

Semantic memory refers to the memory of meanings, understandings, and other concept-based
knowledge, and underlies the conscious recollection of factual information and general
knowledge about the world. Semantic and episodic memory together make up the category of
declarative memory, which is one of the two major divisions in memory. With the use of our
semantic memory we can give meaning to otherwise meaningless words and sentences. We can
learn about new concepts by applying our knowledge learned from things in the past. The
counterpart to declarative, or explicit memory, is procedural memory, or implicit memory.
TLC is an instance of a more general class of models known as semantic networks. In a semantic
network, each node is to be interpreted as representing a specific concept, word, or feature. That
is, each node is a symbol. Semantic networks generally do not employ distributed representations
for concepts, as may be found in a neural network. The defining feature of a semantic network
is that its links are almost always directed (that is, they only point in one direction, from a base
to a target) and the links come in many different types, each one standing for a particular
relationship that can hold between any two nodes. Processing in a semantic network often takes
the form of spreading activation.

Notes Semantic networks see the most use in models of discourse and logical
comprehension, as well as in Artificial Intelligence. In these models, the nodes correspond
to words or word stems and the links represent syntactic relations between them.

13.4.1 Feature Models

Feature models view semantic categories as being composed of relatively unstructured sets of
features. The semantic feature-comparison model, proposed by Smith, Shoben, and Rips (1974),
describes memory as being composed of feature lists for different concepts. According to this
view, the relations between categories would not be directly retrieved, they would be indirectly
computed. For example, subjects might verify a sentence by comparing the feature sets that
represent its subject and predicate concepts. Such computational feature-comparison models
include the ones proposed by Meyer (1970), Rips (1975), Smith, et al. (1974).
Early work in perceptual and conceptual categorization assumed that categories had critical
features and that category membership could be determined by logical rules for the combination
of features. More recent theories have accepted that categories may have an ill-defined or “fuzzy”
structure and have proposed probabilistic or global similarity models for the verification of
category membership.

13.4.2 Associative Models

The “association”—a relationship between two pieces of information—is a fundamental concept
in psychology, and associations at various levels of mental representation are essential to models
of memory and cognition in general. The set of associations among a collection of items in
memory is equivalent to the links between nodes in a network, where each node corresponds to
a unique item in memory. Indeed, neural networks and semantic networks may be characterized
as associative models of cognition. However, associations are often more clearly represented as
an N×N matrix, where N is the number of items in memory. Thus, each cell of the matrix
corresponds to the strength of the association between the row item and the column item.
Learning of associations is generally believed to be a Hebbian process; that is, whenever two
items in memory are simultaneously active, the association between them grows stronger, and
the more likely either item is to activate the other.

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