Representation of Knowledge in Taxonomies and Ontologies and their Application in Advance Analysis to Cognitive Computing

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.