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Knowledge Graph by Ajit Singh
English | August 28, 2025 | ISBN: N/A | ASIN: B0FP2ZNR4Z | 304 pages | EPUB | 0.94 Mb
"Knowledge Graph" is a comprehensive, practical, and student-centric guide designed to navigate the dynamic and powerful world of connected data. This book serves as a one-stop resource for B.Tech and M.Tech students, bridging the gap between foundational theory and cutting-edge, industry-relevant application. It systematically demystifies how to model, build, query, and leverage Knowledge Graphs to create truly intelligent systems.
Key Features of This Book:
1. Beginner to Advanced Trajectory: The 10-chapter structure provides a smooth learning curve, starting from the absolute basics of graphs and moving to advanced topics like Graph Neural Networks (GNNs) and reasoning.
2. Hands-On and Practical: Learning is reinforced through extensive hands-on examples, code snippets (primarily in Python), and practical exercises in every chapter, using industry-standard tools.
3. Complete Capstone Project: Chapter 10 is a comprehensive, live project that guides the reader through building a real-world application from scratch, including data ingestion, querying, and code implementation.
4. Dual Paradigm Coverage: The book provides in-depth coverage of both major Knowledge Graph paradigms: RDF/SPARQL for semantic web applications and Labeled Property Graphs/Cypher (Neo4j) for enterprise applications.
5. Focus on Simplicity and Clarity: Complex theoretical concepts are broken down and explained using simple, jargon-free language and illustrated with relatable, real-life examples.
6. Industry-Relevant Tools & Technologies: Readers will gain practical experience with essential tools and libraries such as Neo4j, Protégé, SPARQL, Python, RDFLib, and spaCy, enhancing their employability.
Who Should Read This Book?
1. B.Tech/M.Tech Students in Computer Science, IT, and Data Science.
2. Software Developers and Engineers looking to integrate knowledge-based AI into their applications.
3. Data Scientists and Analysts wanting to leverage graph-based analytics and build more contextual AI models.
4. AI/ML Enthusiasts interested in understanding the synergy between Machine Learning and Knowledge Graphs.
5. Academic Researchers and self-learners seeking a structured and practical introduction to the field.
Disclaimer: Earnest request from the Author.
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