Knowledge Representation for Artificial Intelligence Applications

Knowledge Representation for AI Applications helps bridge the gap between LLMs and structured knowledge.

While LLMs excel at generating human-like text, they struggle with hallucination, temporal reasoning, and accessing real-time domain knowledge. This comprehensive guide shows AI practitioners how to augment LLMs with structured knowledge representation, creating more reliable, explainable, and accurate AI systems. From foundational RDF triples to cutting-edge Knowledge-Grounded RAG (KRAG) architectures, you'll master practical techniques for building knowledge-enhanced applications that combine neural creativity with symbolic precision.

Written for working AI practitioners, data scientists, and developers, this book bridges the gap between theoretical semantic web concepts and modern AI implementation needs. You'll learn to build production-ready systems using tools like Neo4j, SPARQL, and the emerging Model Context Protocol (MCP), with real-world examples from healthcare, finance, and enterprise applications. Whether you're reducing hallucination in chatbots, building domain-specific AI assistants, or creating explainable AI systems, this book provides the architectural patterns and implementation strategies you need to enhance your AI applications with structured knowledge that scales.