Thesaurus as a complex network

TL;DR

This paper models a thesaurus as a directed complex network, analyzing link distributions and proposing new classification and arrangement methods based on network theory and stochastic growth models.

Contribution

It introduces a network-based representation of thesauri, analyzes link distributions, and proposes a novel arrangement method using the inversion technique.

Findings

Incoming link distribution follows a stretched exponential.

Outgoing link distribution fits a Ricatti differential equation solution.

Proposes a new thesaurus arrangement method.

Abstract

A thesaurus is one, out of many, possible representations of term (or word) connectivity. The terms of a thesaurus are seen as the nodes and their relationship as the links of a directed graph. The directionality of the links retains all the thesaurus information and allows the measurement of several quantities. This has lead to a new term classification according to the characteristics of the nodes, for example, nodes with no links in, no links out, etc. Using an electronic available thesaurus we have obtained the incoming and outgoing link distributions. While the incoming link distribution follows a stretched exponential function, the lower bound for the outgoing link distribution has the same envelope of the scientific paper citation distribution proposed by Albuquerque and Tsallis. However, a better fit is obtained by simpler function which is the solution of Ricatti's differential equation. We conjecture that this differential equation is the continuous limit of a stochastic growth model of the thesaurus network. We also propose a new manner to arrange a thesaurus using the ``inversion method''.