Knowledge Graphs and LLMs in Action is a practical guide to putting knowledge graphs into action. It’s full of techniques and code samples for building and analyzing knowledge graphs, all demonstrated with serious full-sized datasets. Throughout the book, you’ll find extensive examples and use-cases taken from healthcare, biomedicine, document archive management systems, and even law enforcement. You’ll learn methodologies based on the very latest KG approaches, as well as deep learning graph techniques such as Graph Neural Networks and NLP-based tools like BERT.
Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.