Intelligent Knowledge Graph Engineering: Advanced Reasoning, Intelligence, Semantic Retrieval, AI Integration, and Enterprise Systems

Intelligent Knowledge Graph EngineeringAdvanced Reasoning, Graph Intelligence, Semantic Retrieval, AI Integration, and Enterprise SystemsKnowledge graphs can do far more than connect data. When combined with reasoning, machine learning, semantic retrieval, and generative AI, they can become intelligent systems capable of discovering relationships, providing contextual knowledge, and supporting complex decisions.Intelligent Knowledge Graph Engineering explores the technologies and engineering practices required to build these next-generation knowledge systems.Building on the foundations of knowledge representation and graph engineering, this book moves into advanced graph intelligence and AI integration. It examines how structured knowledge can work together with graph algorithms, embeddings, vector search, large language models, Graph RAG, machine learning, and AI agents.You will learn how to: - Design intelligent knowledge graph architectures- Apply advanced semantic modeling and contextual knowledge representation- Build reasoning and inference systems for deriving new knowledge- Use graph algorithms for traversal, similarity, community detection, and pattern discovery- Apply graph analytics to discover hidden structures and relationships- Build graph embeddings and knowledge graph completion systems- Combine semantic search, vector search, and graph-based retrieval- Design Graph RAG pipelines for grounded and contextual AI responses- Integrate knowledge graphs with large language models- Build graph-grounded question-answering and natural-language query systems- Apply graph machine learning and graph neural networks- Use knowledge graphs as persistent memory and context for AI agents- Architect secure, scalable, and observable enterprise graph-AI platforms- Evaluate retrieval, reasoning, generation, reliability, and system performanceThe book focuses on engineering trade-offs and real-world system design, not just individual technologies. It explores when different approaches should be used, how they can be combined, and what challenges emerge around scalability, latency, data quality, provenance, explainability, security, and reliability.Whether you are building a Graph RAG application, semantic search platform, AI assistant, recommendation system, graph machine learning pipeline, or enterprise knowledge platform, this book provides a practical framework for turning connected knowledge into actionable intelligence.Designed for knowledge graph engineers, AI/ML engineers, software and data engineers, graph database developers, search engineers, ontology practitioners, and enterprise architects, this book takes the reader from advanced graph concepts to production-oriented intelligent knowledge systems.Connect knowledge. Reason over relationships. Ground AI in context. Build intelligent systems that understand how information fits together.