Bridging Knowledge, Data, and AI : Harnessing the Semantic Layer Framework to Drive Intelligence

“Chapter II: Semantic Layer Component Design and Implementation” breaks the semantic layer down into its core components (knowledge assets, business glossary, metadata, taxonomy, and knowledge graph) defining each, explaining their role in the semantic layer, discussing associated technologies, and delving into the modeling of these components.

The semantic layer framework is the practical combination of core technologies and best-in-practice design principles that deliver a framework connecting all of an organization's knowledge assets in context and with the greatest accuracy. It combines elements from data, information, and knowledge management in order to enable the reliable (re)use of an organization’s assets. This book guides the reader through every element of the semantic layer, from its core definition and business value, associated components and design requirements, methodological keys to success, realworld success stories and applications, and finally, through its relationship with AI. The book is divided into five parts. “Chapter I: Understanding the Semantic Layer” defines the semantic layer, explains its value, and details business outcomes and potential areas of return on investment. “Chapter II: Semantic Layer Component Design and Implementation” breaks the semantic layer down into its core components (knowledge assets, business glossary, metadata, taxonomy, and knowledge graph) defining each, explaining their role in the semantic layer, discussing associated technologies, and delving into the modeling of these components. “Chapter III: Methodologies for Designing and Integrating the Semantic Layer” delivers a step-by-step guide to the design and implementation of a semantic layer, covering a range of best practices to ensure the success of an initiative. “Chapter IV: Real-World Success Stories” expands on various real-world examples, presenting case studies from a range of organizations that have successfully implemented a semantic layer and detailing the use cases, approaches, and outcomes. Finally, “Chapter V: Powering Artificial Intelligence and Beyond” covers the interrelationship between the semantic layer framework and AI, elaborating on the ways in which these two fields can mutually benefit one another and concluding with a look into the future on how mature organizations are evolving their semantic ecosystems to drive responsible AI adoption and further competitive capabilities. This book is intended for practitioners seeking to implement the semantic layer within their own organization, for those interested in learning how to do so, as well as for executives or business stakeholders that may find themselves overseeing such a program.