Intelligent Knowledge Graph Engineering: Advanced Reasoning, Graph Intelligence, Semantic Retrieval, AI Integration, and Enterprise Systems - Donald Knutth
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Intelligent Knowledge Graph Engineering: Advanced Reasoning, Graph Intelligence, Semantic Retrieval, AI Integration, and Enterprise Systems
Donald Knutth
Synopsis "Intelligent Knowledge Graph Engineering: Advanced Reasoning, Graph 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 architecturesApply advanced semantic modeling and contextual knowledge representationBuild reasoning and inference systems for deriving new knowledgeUse graph algorithms for traversal, similarity, community detection, and pattern discoveryApply graph analytics to discover hidden structures and relationshipsBuild graph embeddings and knowledge graph completion systemsCombine semantic search, vector search, and graph-based retrievalDesign Graph RAG pipelines for grounded and contextual AI responsesIntegrate knowledge graphs with large language modelsBuild graph-grounded question-answering and natural-language query systemsApply graph machine learning and graph neural networksUse knowledge graphs as persistent memory and context for AI agentsArchitect secure, scalable, and observable enterprise graph-AI platformsEvaluate 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.
