This chapter focuses on how ontologies and semantic web technology can be
used in artificial intelligence or systems engineering. Technological trends imply that
future digital technology approaches and tools will use AI and ML technology. Logicbased reasoning and semantic modeling assist in classification, customization, and
relationship detection but struggle to describe how decisions are made. Knowledge
acquisition plays a vital role in using this form of AI. Ontologies are methods for mode
modeling of reasoning domains required for digital fields instantiated in Digital System
Models (DSM). They grow as digital twins and evolve with the physical instantiations
of a DSM over time. Semantic innovations and ontologies codify knowledge of
systems engineering as a prerequisite for reasoning using interoperable ontologies. This
chapter explores the technologies behind advanced analytics and how they can be
leveraged in a knowledge-driven cognitive environment. Advanced analytics help
gaining deeper insights and predict outcomes more accurately and insightfully.
Keywords: Classifications and ontologies, Complexity, Conceptual clustering algorithm, Classification decisions, Descriptive analyses, Google investigators, Intellectual system, Logical notation, Minimal knowledge, MRI machine, Neural nets, Organizational learning, Object-oriented development, Predictive methods, Semantic webs, Sustainability, RDF, Representation knowledge, Syntax, Text analysis, URL.