Linguistic Inspired Graph Analysis (2105.06216v1)

Abstract: Isomorphisms allow human cognition to transcribe a potentially unsolvable problem from one domain to a different domain where the problem might be more easily addressed. Current approaches only focus on transcribing structural information from the source to target structure, ignoring semantic and pragmatic information. Functional Language Theory presents five subconstructs for the classification and understanding of languages. By deriving a mapping between the metamodels in linguistics and graph theory it will be shown that currently, no constructs exist in canonical graphs for the representation of semantic and pragmatic information. It is found that further work needs to be done to understand how graphs can be enriched to allow for isomorphisms to capture semantic and pragmatic information. This capturing of additional information could lead to understandings of the source structure and enhanced manipulations and interrogations of the contained relationships. Current mathematical graph structures in their general definition do not allow for the expression of higher information levels of a source.