Knowledge Graphs and LLMs in Action

Combine knowledge graphs with large language models to deliver powerful, reliable, and explainable AI solutions. Knowledge graphs model relationships between the objects, events, situations, and concepts in your domain so you can readily identify important patterns in your own data and make better decisions. Paired up with large language models, they promise huge potential for working with structured and unstructured enterprise data, building recommendation systems, developing fraud detection mechanisms, delivering customer service chatbots, or more. This book provides tools and techniques for efficiently organizing data, modeling a knowledge graph, and incorporating KGs into the functioning of LLMs—and vice versa. In Knowledge Graphs and LLMs in Action you will learn how to:• Model knowledge graphs with an iterative top-down approach based in business needs• Create a knowledge graph starting from ontologies, taxonomies, and structured data• Build knowledge graphs from unstructured data sources using LLMs• Use machine learning algorithms to complete your graphs and derive insights from it• Reason on the knowledge graph and build KG-powered RAG systems for LLMs In Knowledge Graphs and LLMs in Action, you’ll discover the theory of knowledge graphs then put them into practice with LLMs to build working intelligence systems. You’ll learn to create KGs from first principles, go hands-on to develop advisor applications for real-world domains like healthcare and finance, build retrieval augmented generation for LLMs, and more. about the technologyUsing knowledge graphs with LLMs reduces hallucinations, enables explainable outputs, and supports better reasoning. By naturally encoding the relationships in your data, knowledge graphs help create AI systems that are more reliable and accurate, even for models that have limited domain knowledge. about the readerFor ML and AI engineers, data scientists, and data engineers. Examples in Python.