3 - WHY SEMANTIC NETWORKS?

Abstract

It is often asserted that semantic networks, are mere notational variants of other well-defined and “standard” representation languages. Yet semantic networks seem to have a substantial following and a special appeal of their own. What makes semantic networks special, and why has so much research effort been devoted to developing network-based knowledge representation languages? In this chapter we attempt to answer this question from the perspective of computational effectiveness. We argue that a knowledge representation language must not only be characterized in terms of its representational adequacy but also in terms of its computational effectiveness. Furthermore, a computationally effective knowledge representation framework must explicate the relationship between the nature of representation, the effectiveness of certain inferences and the computational architecture in which the computations/inferences are performed. Semantic networks, or graph-based representation formulations are examples of such knowledge representation frameworks. In particular, semantic networks—realized as massively parallel networks—may provide the appropriate framework for modeling reflexive reasoning—reasoning that we can perform rapidly, effortlessly, and without conscious effort.