Knowledge Graphs

This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book is divided into ten chapters. The  rst chapter provides a general introduction to the area, de nes the concept of a “knowledge graph”, and provides a high-level overview of how knowledge graphs are currently being used. The second chapter presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried. The third chapter describes how the resulting data graph can be enhanced with notions of schema, identity and context. The fourth chapter discusses how ontologies and rules can be used to encode knowledge, and howthey enable deductive forms of reasoning . The  fth chapter delves into how inductive techniques – based on statistics, graph analytics, machine learning, etc. – can be used to encode and extract knowledge . The sixth chapter is dedicated to techniques for the creation and enrichment of knowledge graphs from legacy sources of data. The seventh chapter enumerates a varietyof quality measures that can be used to assess a knowledge graph in terms of its  tness for use in a variety of applications. The eighth chapter presents key methods for the re nement of knowledge graphs, with the goal of improving their completeness and correctness. The ninth chapter provides a survey of the open and enterprise knowledge graphs that have emerged in recent years, along with the industries within which, and the applications for which, they have been most widely adopted. The tenth chapter wraps up the book with discussion of the current limitations and future directions along which knowledge graphs are likely to evolve. An appendix further covers knowledge graphs from an historical perspective, establishing their signi cance in the broader context of the academic study of data and knowledge, as well as surveying prior de nitions of “knowledge graphs” from the literature. The book is aimed at students, researchers and practitioners who wish to learn more about knowledge graphs, and how they facilitate extracting value from diverse data at large-scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal de nitions and extensive references are also provided for those who opt to delve more deeply into speci c topics.