Use of Natural Language Processing in Digital Engineering Context to Aid Tagging of Model

Abstract

This paper uses Natural Language Processing to provide augmented intelligence assistance to the resource intensive task of aligning systems engineering artifacts, namely text requirements and system models, with ontologies. Ontologies are a key enabling technology for digital, multidisciplinary interoperability. The approach presented in this paper combines the efficiency of statistical based natural language processing to process large sets of data with expert verification of output to enable accurate alignment to ontologies in a time efficient manner. It applies this approach to an example from the telecommunications domain to demonstrate the workflows and highlight key points in the process. Enabling easier, faster alignment of systems engineering artifacts with ontologies allows for a holistic view of a system under design and enables interoperability between tools and domains.