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