Table of Contents
Advances in Artificial Intelligence
Volume 2017, Article ID 1948317, 10 pages
https://doi.org/10.1155/2017/1948317
Research Article

iWordNet: A New Approach to Cognitive Science and Artificial Intelligence

1Boston University, 801 Massachusetts Ave, Boston, MA, USA
2Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA

Correspondence should be addressed to Mark Chang; ude.ub@gnahcym

Received 5 April 2017; Revised 18 July 2017; Accepted 28 August 2017; Published 11 October 2017

Academic Editor: António Dourado Pereira Correia

Copyright © 2017 Mark Chang and Monica Chang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

One of the main challenges in artificial intelligence or computational linguistics is understanding the meaning of a word or concept. We argue that the connotation of the term “understanding,” or the meaning of the word “meaning,” is merely a word mapping game due to unavoidable circular definitions. These circular definitions arise when an individual defines a concept, the concepts in its definition, and so on, eventually forming a personalized network of concepts, which we call an iWordNet. Such an iWordNet serves as an external representation of an individual’s knowledge and state of mind at the time of the network construction. As a result, “understanding” and knowledge can be regarded as a calculable statistical property of iWordNet topology. We will discuss the construction and analysis of the iWordNet, as well as the proposed “Path of Understanding” in an iWordNet that characterizes an individual’s understanding of a complex concept such as a written passage. In our pilot study of 20 subjects we used a regression model to demonstrate that the topological properties of an individual’s iWordNet are related to his IQ score, a relationship that suggests iWordNets as a potential new methodology to studying cognitive science and artificial intelligence.