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Description
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.
Chapter 1: Setting the Stage.- Part 1. Foundations of Semantic Knowledge Graph Design.- Chapter 2: What's the Semantic Web?.- Chapter 3: Core Technologies.- Chapter 4: The Data Life Cycle.- Chapter 5: Infrastructure and Standards.- Chapter 6: Requirements Acquisition.- Chapter 7: Conceptual Modeling.- Chapter 8: Design Modeling: From Meaning to Robustness.- Chapter 9: Implementation.- Chapter 10: Governance, Security, and Enrichment.- Part 2. Enterprise Architectures and Organizational Patterns.- Chapter 11: Enterprise Data Products: Microservices for Data.- Chapter 12: Semi-formal Modeling.- Chapter 13: Data Products and Domain-Driven Design: A Graph RAG Data Catalog.- Chapter 14: Semantic Technologies: Industry Applications.- Chapter 15: Conclusion from Models to Systems.
Michael DeBellis is a Semantic Web and AI consultant and researcher with over 40 years of experience in industry and academia. He began designing ontologies in the early 1980s while building expert systems for Accenture and later worked as a researcher at USC s Information Sciences Institute (ISI), where he used Loom a direct predecessor to the Web Ontology Language (OWL). Throughout his career at firms including Deloitte, Accenture, and ThoughtWorks, he has focused on applying advanced technology to real-world enterprise challenges. His work emphasizes the practical integration of Semantic Knowledge Graphs into complex systems, highlighting the importance of user requirements, legacy data, and enterprise architecture over purely theoretical modeling. He has published and presented internationally on a broad range of topics across multiple domains.
