Knowledge graphs help understand relationships between the objects, events, situations, and concepts in your data so you can readily identify important patterns and make better decisions. This book provides tools and techniques for efficiently labeling data, modeling a knowledge graph, and using it to derive useful insights. Knowledge graphs represent a network of real-world entities–from people and places to genes and proteins–and model the relationships between them. KGs represent a real paradigm shift in the way that machines can understand data by effectively modeling the contextual information that’s vital for human knowledge. They’re poised to help revolutionize data analysis and machine learning, with applications ranging from search engines to e-commerce and more. 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. For readers who know the basics of Machine Learning. Examples in Python.

Knowledge Graphs and LLMs in Action (MEAP) PDF

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