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.
In Knowledge Graphs Applied
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
- Use machine learning algorithms to hone and complete your graphs
- Build knowledge graphs from unstructured text data sources
- Reason on the knowledge graph and apply machine learning algorithms
Move beyond analyzing data and start making decisions based on useful, contextual knowledge. The cutting-edge knowledge graphs (KG) approach puts that power in your hands. In Knowledge Graphs Applied
, you’ll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. You’ll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance.
about the technology
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.
about the book
Knowledge Graphs Applied
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.
about the reader
For readers who know the basics of machine learning. Examples in Python.
about the authors
Dr. Alessandro Negro
is the Chief Scientist at GraphAware. Alessandro has been a speaker at many prominent conferences and author of the Manning book Graph-Powered Machine Learning and several scientific publications. 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 and a Fellow at the Nexa Center for Internet & Society. He holds a Ph.D. in Computer Engineering from the Politecnico di Torino (Italy), where he explored Graph Representation Learning techniques to support the automatic building of Knowledge Graphs.
is the Lead Machine Learning Engineer at GraphAware. He holds a master’s degree in software engineering from Unisalento (Italy). As a bridge between science and industry, he assists with moving rapidly from scientific reasoning to product value.