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		<h2 id="preface">Preface</h2>
		<p>The origins of this book can be traced back to a Dagstuhl Seminar, held in 2018, on the topic of Knowledge Graphs. At the time of the seminar, the topic was quickly becoming mainstream in academia and industry, but there were conflicting messages as to what a “knowledge graph” was. Much of the discussion of the seminar centred on this question, and there were divergent opinions as to how knowledge graphs could (or should) be defined; how they relate to previous concepts such as graph databases, knowledge bases, ontologies, RDF graphs, property graphs, semantic networks, etc.; and how the emerging area of Knowledge Graphs should be positioned with respect to the established areas of Artificial Intelligence, Big Data, Databases, Graph Theory, Logic, Machine Learning, Knowledge Representation, Natural Language Processing, Networks (in their various forms), and the Semantic Web. As the discussion continued, a consensus began to emerge: Knowledge Graphs, as a topic, involves a novel confluence of techniques stemming from previously disparate scientific communities, with the unifying goal of developing novel graph-based techniques for better integrating and extracting value from diverse knowledge sources at large scale.</p>
		<p>As a follow-up to the seminar, the attendees agreed that in order to foster this unifying view of Knowledge Graphs, there was a need for a manuscript that would serve as a general introduction to the area. This manuscript would:</p>
		<ul>
			<li>motivate knowledge graphs and the value of abstracting data as graphs;</li>
			<li>survey the historical context of knowledge graphs and the key initiatives leading to their popularisation;</li>
			<li>draw together disparate views of knowledge graphs into a unifying definition;</li>
			<li>provide an introduction to the key techniques that knowledge graphs enable, relating to querying, validation, reasoning, learning, refinement, enrichment, quality assessment, and more besides;</li>
			<li>describe how knowledge graphs are used in practice, surveying the companies using knowledge graphs, the applications they are used for, the open knowledge graphs that have been published, etc.;</li>
			<li>delineate future research directions for knowledge graphs.</li>
		</ul>
		<p>The manuscript would then serve as an introductory text for students, practitioners and researchers new to the area, helping to form a consensus in terms of what is a knowledge graph, laying the foundations for future developments.</p>
		<p>The goal of preparing this manuscript was an ambitious one, and involved drawing together and distilling down a vast amount of literature on a diverse range of topics into a set of key concepts described in an accessible way. For this reason, the manuscript has been prepared by many authors, who have lent their knowledge and expertise to the preparation of specific sections. A short version of the manuscript was first published as a tutorial paper&nbsp;<?php echo $references->cite("HoganBCdMGKGNNN21"); ?>, consisting of an abridged version of the first five chapters of this book, along with a summary of how knowledge graphs are used in practice, and conclusions. However, there was not enough space to describe all of the important developments in the area. This led us to publish this book, which further includes topics relating to the creation, enrichment, quality assessment, refinement and publication of knowledge graphs, as well as formal definitions, a historical perspective, and extended discussion throughout.</p>
		<p>The book is divided into ten chapters. The first chapter provides a general introduction to the area, defines 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 how they enable deductive forms of reasoning. The fifth 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 variety of quality measures that can be used to assess a knowledge graph in terms of its fitness for use in a variety of applications. The eighth chapter presents key methods for the refinement 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 significance in the broader context of the academic study of data and knowledge, as well as surveying prior definitions of “knowledge graphs” from the literature.</p>
		<p>A key aim of this book is to be accessible to a broader audience. While background knowledge of related topics such as Databases, Logic, Machine Learning, Semantic Web, etc., will help to understand some of the particular topics mentioned, such a background is not necessary to follow the general concepts described within. The book aims to motivate and illustrate the various concepts it introduces from a practical perspective, and in order to be as accessible as possible, relies heavily on an example-driven presentation using a graphical notation. For the reader wishing to dig more into the technical minutiae, we complement this discussion with formal definitions throughout; however, the reader more interested in understanding the general concepts and their rationale will find the discussion to be self-contained if they choose to skip the definitions presented in visually distinctive boxes.</p>
		<p>The book serves as an entry point for those new to the topic, and may thus serve as a useful textbook for university courses, for researchers who are venturing into the topic for the first time, and for practitioners who wish to understand more about how knowledge graphs might be of use within their company or organisation, or indeed, how to maximise the value of the knowledge graphs that they are currently developing. Readers who are already active within specific sub-areas of Knowledge Graphs may further appreciate the technical definitions included, the references to other literature provided, and the broader perspective that this book offers in terms of the other related sub-areas and how they complement each other.</p>
		<p>By drawing together diverse techniques from disparate areas, Knowledge Graphs has become an exciting topic in terms of both research and applications. We expect to see growing interest on this topic as the years advance, and indeed hope that this book will help to more firmly establish the foundations of this topic, and to foster future developments upon these foundations, potentially by its readers.</p>
		<p style="text-align: right; font-style: italic;">Aidan&nbsp;Hogan, Eva&nbsp;Blomqvist, Michael&nbsp;Cochez, Claudia&nbsp;d’Amato, Gerard&nbsp;de&nbsp;Melo, Claudio&nbsp;Gutierrez, Sabrina&nbsp;Kirrane, José&nbsp;Emilio&nbsp;Labra&nbsp;Gayo, Roberto&nbsp;Navigli, Sebastian&nbsp;Neumaier, Axel-Cyrille&nbsp;Ngonga&nbsp;Ngomo, Axel&nbsp;Polleres, Sabbir&nbsp;M.&nbsp;Rashid, Anisa&nbsp;Rula, Lukas&nbsp;Schmelzeisen, Juan&nbsp;Sequeda, Steffen&nbsp;Staab, Antoine&nbsp;Zimmermann<br/>
		September 2021</p>
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