Knowledge graphs are a pivotal building block for designing intelligent systems for empowering advanced analytics and decision-making. They make it possible to build solutions that engineers, data scientists, and CEOs alike will value.
Knowledge Graphs and LLMs In Action is a practical guide to combining knowledge graph technology with large language models. It contains techniques, code samples and use cases that will help you leverage the connected nature of various data sources and simultaneously incorporate human knowledge.
You’ll learn how to…
- Model knowledge graphs with an iterative top-down approach, based on 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 knowledge graph-powered RAG systems for LLMs
Meet the authors
Both authors have extensive experience in the domain of building and analysing knowledge graphs. Together they cover expertise in engineering, research, data science and consultancy, all delivered for and with clients operating in a wide range of industries. Currently they are contributing to building an enterprise-level product for mission-critical graph analytics – GraphAware Hume.
Alessandro Negro
Chief Scientist
Fabio Montagna
Lead Machine Learning Engineer
In Knowledge Graphs and LLMs in Action, you’ll dive into the theory of knowledge graphs and learn how to apply them alongside large language models (LLMs) to build real-world intelligence systems.
The book takes you from creating knowledge graphs from first principles to developing intelligent advisor applications for domains like healthcare and finance, and even constructing retrieval-augmented generation (RAG) pipelines for LLMs.
