Knowledge Graphs and LLMs in Action: (In Action)

Data overload, disconnected context, and stalled machine learning results are common frustrations for data teams. Even with vast datasets and advanced models, insights remain elusive when information is scattered and relationships are unclear. What if you could structure your data in a way that gives it meaning, connects the dots, and powers smarter, faster learning? By building knowledge graphs that integrate with large language models, you can transform disconnected information into actionable, context-rich intelligence that drives real results. Iterative top-down modeling: Aligns every graph decision with clear business questions. Ontology and taxonomy starters: Jump-start graph design from your existing structured data. Python code walk-throughs: Let you replicate techniques on day one, no guesswork. GNN and BERT integration: Upgrade graphs with deep learning for smarter reasoning and predictions. Real healthcare and policing cases: Prove scalability on messy, high-stakes datasets. Knowledge Graphs and LLMs in Action by GraphAware scientists Dr. Alessandro Negro and colleagues delivers a code-rich softcover reference that unites cutting-edge research with field-tested engineering practice. Starting with business questions, you model ontologies, import varied sources, then iteratively expand your graph. Later chapters layer GNNs, transformers, and reasoning algorithms, showing complete pipelines on full-scale datasets. You will leave with repeatable workflows, reusable code, and the confidence to connect fragmented data into intelligent, context-aware applications. Stop guessing; start delivering measurable machine learning impact.

Table of Contents:
PART I: FOUNDATIONS OF HYBRID INTELLIGENT SYSTEMS 1 KNOWLEDGE GRAPHS AND LLMS: A KILLER COMBINATION 2 INTELLIGENT SYSTEMS: A HYBRID APPROACH PART II: BUILDING KNOWLEDGE GRAPHS FROM STRUCTURED DATA SOURCES 3 CREATE YOUR FIRST KNOWLEDGE GRAPH FROM ONTOLOGIES 4 FROM SIMPLE NETWORKS TO MULTI-SOURCE INTEGRATION PART III: BUILDING KNOWLEDGE GRAPHS FROM TEXT 5 DOMAIN-SPECIFIC KNOWLEDGE EXTRACTION FROM UNSTRUCTURED DATA 6 BUILDING KNOWLEDGE GRAPHS WITH LARGE LANGUAGE MODELS 7 NAMED ENTITY DISAMBIGUATION 8 NED WITH OPEN LLMS AND DOMAIN ONTOLOGIES 9 MACHINE LEARNING ON KNOWLEDGE GRAPHS: A PRIMER APPROACH PART IV: MACHINE LEARNING ON KNOWLEDGE GRAPHS 10 GRAPH FEATURE ENGINEERING: MANUAL AND SEMI-AUTOMATED APPROACHES 11 GRAPH REPRESENTATION LEARNING AND GRAPH NEURAL NETWORK 12 NODE CLASSIFICATION AND LINK PREDICTION WITH GNNS 13 KNOWLEDGE GRAPH-POWERED RETRIEVAL AUGMENTED GENERATION PART V: INFORMATION RETRIEVAL WITH KNOWLEDGE GRAPHS AND LLMS 14 ASK A KG WITH NATURAL LANGUAGE 15 BUILDING AQA AGENT WITH LANGGRAPH APPENDIXES APPENDIX A: INTRODUCTION TO GRAPHS APPENDIX B: NEO4J APPENDIX C: BUILD KNOWLEDGE GRAPHS FROM STRUCTURED SOURCES

About the Author :
Dr. Alessandro Negro is the Chief Scientist at GraphAware. He is one of the creators of GraphAware Hume, a mission critical knowledge graph platform.  Dr. Vlastimil Kus is the Lead Data Scientist at GraphAware where he contributes to the development of Hume. Over the years he gained significant experience in building and utilizing Knowledge Graphs from unstructured data using NLP and ML techniques in various domains. His current focus is NLP and Graph Machine Learning. Dr. Giuseppe Futia is Senior Data Scientist at GraphAware. He studied Graph Representation Learning techniques to support the automatic building of Knowledge Graphs. Fabio Montagna is the Lead Machine Learning Engineer at GraphAware. As a bridge between science and industry, he assists with moving rapidly from scientific reasoning to product value.

Review :
Many resources cover KGs or LLMs separately, but few combine both with this much clarity and practical detail. For technical professionals looking to implement hybrid systems, this book bridges the gap. Sophia Shvets, Data Scientis, NinjaTech This book is excellent for anyone who really wants to know how and why LLMS work with Knowledge Graphs. It goes deep into the underlying technologies in play, in NLP and ML too, providing a good bedrock for understanding. Charles Ivie, Sr. Architect, Amazon Web Services