What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics.
 

In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data.

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  •  Understand the fundamental concepts, phenomena, and processes related to semantic data modeling
• •  Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools
• •  Avoid mistakes and bad practices that can undermine your efforts to create good data models
• •  Learn about model development dilemmas, including representation, expressiveness and content, development, and governance
• •  Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges
  
  Knowledge graphs, ontologies, taxonomies, and other types of semantic data models have been developed and used in the data and artificial intelligence (AI) world for several decades. Their use captures the meaning of data in an explicit and shareable way, and enhances the effectiveness of data-driven applications. In the past decade, the popularity of such models has particularly increased. For example, the market intelligence company Gartner included knowledge graphs in its 2018 hype cycle for emerging technologies; and several prominent organizations like Amazon, LinkedIn, BBC, and IBM have been developing and using semantic data models within their products and services.
   

This book shows you what semantic data modeling entails, and what challenges you have to face as a creator or user of semantic models. More importantly, it provides you with concrete advice on how to avoid dangers (pitfalls) & overcome obstacles (dilemmas).
 

It teaches you some fundamental and enduring semantic modeling principles that remain true, no matter which particular framework or technology you are using, and shows you how you can apply these in your specific context.
 

After reading this book, you will be able to critically evaluate and make better use of existing semantic models and technologies, make informed decisions, and improve the quality and usability of the models you build.

Who Should Read This Book

This book is for data practitioners who develop or use semantic representations of data in their everyday jobs (knowledge engineers, information architects, data engineers, data scientists, etc.), and for whom the explicitness, accuracy, and common understandability of the data’s meaning is an important dimension of their work.

You will find this book particularly useful if you recognize yourself in one or more of the following situations:

• •  You are a taxonomist, ontologist, or other type of data modeler who knows a lot about semantic data modeling, though mostly from an academic and research perspective. You probably have a PhD or MSc in the field and excellent knowledge of modeling languages and frameworks, but you have had little chance to apply this knowledge in an industrial setting. You are now in the early stages of an industrial role and you have the opportunity to apply your knowledge to real-world problems. You have started realizing, though, that things are very different from what the academic papers and textbooks describe; the methods and techniques you’ve learned are not as applicable or effective as you thought. You face difficult situations for which there is no obvious decision to be made and, ultimately, the semantic models you develop are misunderstood, misapplied, or provide little added value. This book will help you put your valuable and hard-earned knowledge into practice and improve the quality of your work.
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  •  You are a data or information architect, tasked with developing semantic models that can solve the problem of semantic heterogeneity between the many disparate data sources and applications or products that your organization has. For that, you have already applied several out-of-the-box semantic data management solutions that promised seamless integration, but the results you got were mostly unsatisfactory. This book will help you to better understand the not so obvious dimensions and challenges you need to address in order to achieve the semantic interoperability you want.
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  •  You are a data scientist, expert in machine learning and statistical data analysis, and part of a multidisciplinary team that builds semantic models for AI applications (e.g., knowledge graphs for virtual assistants). You interact daily with ontologists, linguists, and other semantic professionals, but you struggle to understand their lingo and how your skills can be combined with theirs. This book will introduce you to the basics of semantic data modeling and will help you identify the aspects of it where your expertise can have the biggest impact.
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  •  You are a data scientist, expert in machine learning and statistical data analysis, working with data that has been created and semantically described by other people, teams, and organizations. Often, you are unsure about what these semantic data models really represent and whether they are appropriate for the kind of analysis you want to make or solution you want to build. Even worse, you make incorrect assumptions about the data’s semantics, ending up with machine learning models and data science solutions that do not work as you had expected. This book will show you how to be more critical toward the semantic models you work with and anticipate/tackle problems that may occur.

In a nutshell, this is a book for a broad range of data professionals who want to learn how to “talk semantics” in order to work more effectively together and increase the quality, usability, and value of their data.

About the Author

Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, he currently works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain.

Panos has published several papers at international conferences, journals and books, and he is a regular speaker and trainer in both academic and industry venues, striving to bridge the gap between academia and industry so that they can benefit from each other.