Representing Mental Spaces and Dynamics of Natural Language Semantics

Abstract

Building systems with the robustness of human reasoning capabilities requires inspirations from cognitive science.  The primary objective of this study is to investigate the possibility of representing some basic principles of cognitive semantics’ Mental Spaces Theory such as domain construction, reality status of domains and their elements, and mental attitudes in a knowledge representation framework for the purpose of developing cognitively plausible knowledge representation systems. The model used as the basis of representation is the extended version of conventional semantic networks, namely Multi-Layered Extended Semantic Networks (MultiNet). The data used in this study have been selected from English expressions and have been represented in MWR, MultiNet’s knowledge representation software. Results obtained from analysis of represented data and their comparison to principles of mental spaces theory shows that theoretical constructs of mental spaces theory such as domain construction, reality status of domains and their elements, and mental attitudes can be formally represented in the MultiNet framework.