Knowledge Graph

By: Ajit Singh

Narrated by: Virtual Voice

"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.

Kindly go through the table of contents and refer kindle edition for a glance on the related contents.

Thank you for your kind consideration!