Knowledge Graphs: Fundamentals, Techniques, and Applications (Adaptive Computation and Machine Learning)

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.

Mayank Kejriwal is Research Assistant Professor at the University of Southern California's Viterbi School of Engineering. Craig Knoblock is Executive Director of the Information Sciences Institute at the University of Southern California, where he is also Research Professor of both Computer Science and Spatial Sciences as well as Director of the Data Science Program. Pedro Szekely is Principal Scientist and Director of the Center On Knowledge Graphs at the University of Southern California's Information Sciences Institute.