Knowledge Representation with Ontologies and Semantic Web Technologies to Promote Augmented and Artificial Intelligence in Systems Engineering
Thomas Hagedorn, Mary Bone, Benjamin Kruse, Ian R. Grosse, Mark Blackburn
Abstract
ABSTRACT This article discusses knowledge representation using ontologies and semantic web technologies to enable artificial intelligence (AI) for Systems Engineering. Technology trends indicate new methods and tools for digital engineering will incorporate AI and machine learning (ML) technologies. ML techniques support classification, clustering, and association identification, but struggle to explain the rationale for decision making, where multi‐domain semantic modeling and rule‐based reasoning can excel. Knowledge representation plays a key role in applying this type of AI. Ontologies are a means to domain modeling and reasoning required across Digital Thread domains instantiated in digital system models (DSM). These evolve over time as digital twins, which co‐evolve with physical instantiations of a DSM. Semantic technologies and ontologies formalize knowledge as an enabler for reasoning, with interoperable ontologies enabling reason about systems engineering across domains.