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Paperback Knowledge Graphs in Practice: Designing Semantic Data Models for Scalable Intelligence, Search, and AI Systems Book

ISBN: B0GG6YLFT2

ISBN13: 9798243470216

Knowledge Graphs in Practice: Designing Semantic Data Models for Scalable Intelligence, Search, and AI Systems

Knowledge Graphs in Practice is a clear, hands-on guide to building intelligent data systems that understand relationships, context, and structure. Written for developers, data engineers, architects, and technically curious professionals, this book explains how knowledge graphs transform disconnected information into powerful, queryable insight.
Rather than treating data as isolated tables or documents, knowledge graphs model the real world: entities, connections, and semantics. This book shows you how to design, construct, and evolve graph-based systems that support advanced search, analytics, and artificial intelligence-without unnecessary theory or academic complexity.
You'll explore how modern organizations use knowledge graphs to unify data sources, improve discoverability, enable reasoning, and support machine learning workflows. Practical explanations are paired with real-world patterns to help you move from concept to implementation with confidence.
Inside this book, you'll learn how to: Understand the core principles of graph-based data modelingDesign semantic schemas that scale with growing datasetsRepresent entities, relationships, and context effectivelyIntegrate structured and unstructured informationEnable intelligent querying and inferenceSupport AI, recommendation, and decision systemsAvoid common design and performance pitfallsThis book focuses on clarity, durability, and real-world application. Whether you're modernizing legacy systems, building data platforms, or exploring semantic technologies for the first time, you'll gain a practical framework you can apply immediately.
Knowledge Graphs in Practice is not tied to a single tool or vendor. Instead, it teaches enduring concepts that remain relevant across platforms, industries, and use cases-making it a long-term reference for anyone serious about intelligent data architecture.
If you want to move beyond raw data and start building systems that truly understand information, this book is your guide.

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