Knowledge Graphs and LLMs: Building Intelligent, Explainable, and Context-Aware AI Systems

Unlock the full potential of artificial intelligence by seamlessly integrating Knowledge Graphs with Large Language Models (LLMs) to build smarter, explainable, and context-aware AI systems. This comprehensive guide empowers data scientists, AI engineers, and researchers to harness the synergy of structured knowledge and advanced natural language understanding, creating AI applications that reason, explain, and adapt like never before.

What You'll Learn:

- Foundations of Knowledge Graphs, LLMs, and their hybrid architectures
- Techniques to enhance AI explainability and trustworthiness through transparent reasoning
- Scalable system designs for deploying robust Knowledge Graph + LLM solutions
- Advanced graph indexing, vector databases, and retrieval optimization for performance at scale
- Real-world applications in healthcare, finance, legal, and research domains
- Ethical considerations, bias mitigation, and AI governance best practices

Why This Book?

Unlike other AI texts, this book uniquely focuses on the intersection of Knowledge Graphs and LLMs, providing practical insights into how these technologies combine to overcome challenges in explainability, context awareness, and scalability. Featuring hands-on projects, real-world case studies, and clear code examples, it guides you from foundational concepts to cutting-edge implementations.

Who This Book Is For:

- AI/ML Engineers and Developers seeking to build intelligent, explainable systems
- Data Scientists and Knowledge Engineers working with semantic web and graph data
- Researchers and Graduate Students specializing in AI, NLP, and graph technologies
- Technical leaders aiming to deploy scalable and trustworthy AI solutions

Master the art of building AI that truly understands and explains its decisions - revolutionize your approach with Knowledge Graphs and LLMs.