Fundamentals, Techniques, and Applications
A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence.
The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.
After presenting introductory and background material, the text covers techniques for constructing knowledge graphs, adding new knowledge to (or refining old knowledge in) knowledge graphs, and accessing (or querying) knowledge graphs. Finally, the book describes specific knowledge graph ecosystems, with each ecosystem corresponding to several real-world applications and case studies. Each chapter concludes with a software and resources section as well as bibliographic notes that suggest required reading. End-of-chapter exercises, 130 in all, represent various levels of abstraction.
Hardcover$55.00 X ISBN: 9780262045094 560 pp. | 7 in x 9 in 52 b&w
“Knowledge graphs have recently become a key component for data management in leading technology companies. They are being used now in many application areas, and the topic will only grow in importance in the future.”
Lloyd T. Smith Creativity in Engineering Chair,Department of Computer Science, Kansas State University; Director of the Center for Artificial Intelligence and Data Science (CAIDS)
“A comprehensive and thorough guide covering every aspect of building and using knowledge graphs. Read the book and learn everything that you need to know to apply knowledge graphs in practice.”
Research Scientist, Google