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28 lines
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<title>Designing and Building Enterprise Knowledge Graphs</title>
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<section epub:type="preface">
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<h1 class="fmtitle" epub:type="title"><span epub:type="pagebreak" id="page_xv" title="xv"/>Preface</h1>
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<p class="noindent">Here, we try to answer the simple question: Why did we write this book?</p>
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<p class="indent">Every organization has problems with data. If you say your organization does not, you are either fooling yourself or you are not paying attention. While there are many problems with “modern” data practice, in this book we will focus on the fact that <i>data</i> and <i>knowledge</i> are by and large disconnected, and we want to report on our long quest to connect the two (spoiler: knowledge graphs will bring relief to this).</p>
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<p class="indent">Please understand that this is an <i>opinionated</i> book, based on our own experience in designing and building knowledge graph systems and helping others do the same. Ora was a co-author of the original W3C RDF specification from 1997, and co-authored the seminal <i>Scientific American</i> article on the Semantic Web in 2001. Juan has transfered technology (from a university research project to production in various enterprises) that integrates relational databases with Semantic Web technologies. We have particpated in numerous projects (successful and sometimes not so successful) in domains such as e-commerce, finance, energy, and pharmaceutical. We truly believe that knowledge graphs are the ideal way of managing enterprise data because of the capabilities of connecting knowledge and data at scale.</p>
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<p class="indent">Knowledge graphs are “hot” right now, and thus we see a lot of people jumping on the knowledge graph bandwagon without necessarily understanding how to leverage these new technologies. And funny enough, everyone is talking about “semantics” without understanding what that term really <i>means.</i> Time and again, we see companies get excited about knowledge graphs, jump to the conclusion that it is simply the same thing as a graph database, and roll up their sleeves without applying a principled framework of how to design and build a knowledge graph. We have seen people use graph databases without considering modeling questions. We have seen people believe that AI can automate everything. Without a principled, well thought-out approach, many organizations go about their knowledge graph projects in haphazard, ad hoc manner. We want to avoid early adopters getting <i>too excited</i>, jumping into the deep end, and possibly drowning due to the lack of a principled framework and guidelines. What we would like to avoid is some kind of “knowledge graph winter” (cf. AI winter), a backlash against these promising technologies that could happen if enough people attempt adoption without a clear, principled approach. This book is the result of our lessons learned, and provides a framework that works in practice within the enterprise.</p>
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<p class="indent">The focus of this book is on relational databases as a source of the knowledge graph. A vast amount of knowledge graph literature is based on the assumption of automating the creation of a knowledge graph from unstructured data (text) and semi-structured data (web pages, web tables, open data, logs, etc). These are very challenging problems that Tech Giants and the FAANG <span epub:type="pagebreak" id="page_xvi" title="xvi"/>companies (Facebook, Amazon, Apple, Netflix, Google) encounter. Various comprehensive surveys and books have been published on this topic. However, not all organizations have the same challenges as the tech giants. The majority of critical data resides in relational databases that power applications such as Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), and Order Management Systems (OMS). Furthermore, data warehouses are specialized relational databases. While data lakes enable storage of semi-structured data, the main ways of access are through SQL interfaces. There is a lack of literature that focuses on the day-to-day problems that organizations face when building knowledge graphs from their main type of source: relational databases. This book aims to fill that gap.</p>
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<p class="indent">We think that if you are reading this book, you already want to leverage graph technologies. If in your mind the question of “relational vs. graph” is still at the forefront, you may not be ready for this book. Don’t be one of those people!</p>
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<p class="indent">The goal of our book is to offer a principled framework as well as guidance that combines people, processes, and technology to build and design enterprise knowledge graphs from relational databases. The book is structured in the following manner.</p>
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<p class="numlist">1. We introduce graphs, giving you some background as well as motivating examples why the “old ways” may not work so well. The last skeptics will now become believers.</p>
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<p class="numlist">2. We discuss design of knowledge graphs.</p>
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<p class="numlist">3. We give you a set of <i>mapping patterns</i> that let you move from relational to graphs. You need this library.</p>
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<p class="numlist">4. Not everything can be automated, so we help you build your knowledge graphs by explaining that to do this, you need <i>people,</i> a <i>process,</i> and <i>tools.</i> The people part is important here, as you must understand what roles are needed in a modern knowledge graph practice.</p>
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<p class="numlist">5. At the end, a brief look into the future and some final words of wisdom.</p>
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<p class="indent">Welcome and good luck! We are glad you are here.</p>
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<p class="noindentt1">Juan Sequeda and Ora Lassila</p>
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<p class="noindent">July 2021</p>
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