Knowledge Graphs and LLMs: Building Intelligent, Explainable, and Context-Aware AI Systems
Begin your journey into the future of AI where intelligence meets transparency.
This foundational volume, the first in the Explainable & Context-Aware AI Systems series, introduces the revolutionary synergy between Knowledge Graphs (KGs) and Large Language Models (LLMs). It's the essential guide for data scientists, AI engineers, and researchers eager to build smarter, explainable, and context-aware AI systems that truly understand and adapt.
What you'll master in this foundational guide:
Core Concepts: Grasp the fundamental principles of Knowledge Graphs, Large Language Models, and their powerful hybrid architectures.
Explainability Foundations: Understand how to enhance AI explainability and trustworthiness by leveraging transparent reasoning.
Scalable System Design: Explore scalable architectures and system designs for deploying robust KG-LLM solutions in enterprise environments.
Advanced Data Handling: Dive into graph indexing, vector databases, and retrieval optimization techniques for high-performance at scale.
Real-World Applications: Discover compelling use cases across healthcare, finance, legal, and research domains.
Responsible AI: Address crucial ethical considerations, bias mitigation, and AI governance best practices from the ground up.
Unlike other AI texts, this book uniquely focuses on the critical intersection of Knowledge Graphs and LLMs, providing the theoretical framework and initial practical insights into how these technologies combine to overcome challenges in explainability, context awareness, and scalability. Master the art of building AI that truly understands and explains its decisions-revolutionize your approach with this essential guide.
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