Yet many teams hesitate to adopt these technologies, often viewing them as overly complex or academic. This book aims to change that perception by showing how these tools can be used practically and effectively in real-world systems. From constraint validation to domain modeling to query and inference, you'll learn how Semantic Web standards can help you work with messy, evolving, and interconnected data.

Designing Semantic Knowledge Graphs is a hands-on guide for software engineers, architects, and data professionals who want to design and build semantic models that align with modern enterprise needs. You'll learn how to bridge domain models with real data, create agile ontologies that evolve with your systems, and automate the transformation of existing data sources into knowledge graph form. Along the way, you'll explore ontology design patterns, leverage validation rules with SHACL, and integrate your knowledge graph with tools like LLMs and SPARQL for powerful query and reasoning capabilities.

All techniques are illustrated using free and widely used tools like Protégé and the community edition of AllegroGraph.

You Will: 

• Learn how Semantic Web standards like OWL, RDF, SPARQL, SHACL, and SWRL fit into modern data architecture
• Understand techniques for modeling domains that balance top-down structure with bottom-up data realities
• Explore common ontology design patterns
• Learn strategies for data ingestion, transformation, and validation at scale
• Learn how to create agile models that support change, iteration, and evolving requirements
• Understand how to integrate knowledge graphs with other components like Large Language Models and APIs

Who is it for:

Developers, data scientists, software architects, and engineers who work with structured or semi-structured data and want to build smarter, more adaptable systems. This book will also be useful to product managers, analysts, and consultants seeking better insight into their organization's data strategy.