Semantic Web technologies such as OWL, RDF/RDFS, SPARQL, and SHACL, offer powerful tools for managing data in flexible, meaningful, and scalable ways. As organizations increasingly adopt modern data architectures such as Data Mesh and Data Fabrics, Semantic Knowledge Graphs can serve as a unifying semantic layer, making data easier to integrate, validate, and utilize with explicit knowledge representation and automated reasoning.

Yet many teams hesitate to adopt these technologies, viewing them as overly complex or academic. This book aims to challenge that perception by showing how these tools can be used practically and effectively in real-world systems. From requirements to models to code, you’ll learn how Semantic Web standards can help you work with messy, evolving, real world data.

Designing Semantic Knowledge Graphs is a hands-on guide for software engineers and data professionals who want to design and build semantic models that align with modern enterprise needs and integrate with Large Language Models. All techniques are illustrated using state-of-the-art free tools. All the example ontologies, SHACL models, SPARQL queries, and Python code are available on GitHub under an open source license.

You Will:

See how Semantic Web standards such as OWL, RDF, SPARQL, SHACL, and SWRL enable modern data architectures such as Data Mesh and Data Fabrics
Model domains in ways that balance top-down conceptual structure with bottom-up data realities
Develop strategies for data ingestion, transformation, and validation at scale
Integrate knowledge graphs with Large Language Models and automated reasoning tools

This Book is 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.

Designing Semantic Knowledge Graphs

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