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.