Item Details

The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth ontoligent 2017-11-26T15:09:55Z 2017-11-26T15:09:55Z HK53ZEGH 4687 journalArticle Steyvers and Tenenbaum 2005-01-02 2
Type Journal Article
Author Mark Steyvers
Author Joshua B. Tenenbaum
Volume 29
Issue 1
Pages 41-78
Publication Cognitive Science
ISSN 1551-6709
Date January 2, 2005
DOI 10.1207/s15516709cog2901_3
Accessed 2017-11-26 15:09:55
Library Catalog Wiley Online Library
Language en
Abstract We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks.
Short Title The Large-Scale Structure of Semantic Networks