A practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world.

Demonstrates what pitfalls to avoid and what dilemmas to break if you want to build and exploit high-quality and valuable taxonomies, ontologies, knowledge graphs and other types of semantic data models.

Where to Buy

You can read the book online on the O’Reilly Learning Platform (needs subscription) or you can buy it in electronic and/or print format in the venues below. If, for any reason, you have problems finding the book please leave a note.

Why this book is different

This book does not attempt to give you detailed instructions on how to develop a semantic data model from scratch or how to use specific semantic modeling languages and frameworks. Instead, it takes a helicopter view of the semantic modeling life cycle, discusses fundamental principles and challenges and zooms in on particular issues and situations that deserve your attention and require careful treatment.

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Semantic thinking, not just languages or tools

Most textbooks and tutorials on semantic modeling assume that producing good semantic models is primarily a matter of using the right language or tool. This book, instead, teaches you the necessary principles and techniques to use the available modeling language or framework correctly, in an effort to avoid the “Garbage In, Garbage Out effect”

What doesn’t work

Knowing what doesn’t work and why can be a more effective way to improve the quality of a system or process, compared to knowing only what does work, in theory or in only some cases. This book applies this principle on the task of semantic data modeling by focusing on a) identifying as many ways as possible in which things can go wrong, b) what would be the consequences of that, and c) what could be done to avoid such situations.

Non-boolean phenomena

Most semantic modeling methodologies and frameworks assume that all human knowledge can be separated into false and true statements, and provide little support for tackling “noisy” phenomena like vagueness or uncertainty. The real world, however, is full of such phenomena and this book help you not merely handle them, but actually use them to your advantage.

Decisions in context

Semantic data modeling is challenging, and modelers face many types of dilemmas for which they need to make decisions. Describing successful yet isolated experiments, or “success stories,” rarely helps break these dilemmas. This book focuses on identifying as many difficult situations as possible and showing you how to break through them in your own context

Who should read this book

This book is for data practitioners who develop or use semantic representations of data in their everyday jobs and for whom the explicitness, accuracy, and common understandability of the data’s meaning is an important dimension of their work

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Ontologists, Taxonomists, Knowledge Engineers and Data Modelers

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 are now in your early steps in an industrial role and you have started realizing 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 in practice your valuable and hard-earned knowledge and improve the quality of your work

Data, Information and Knowledge Architects

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.

Data Scientists, Machine Learning Engineers and NLP Specialists

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. Pretty often you find yourself being 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.

What people are saying

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Not only for Semantic Web and ML practitioners, this book illuminates the critical subject of how our clarity and precision in our language and thinking interoperate to make a tremendous impact on the fitness of our software. Highly recommended for analysts, architects, programmers—anyone in software development.

Eben Hewitt

This book is going to become the definitive reference for everyone who works with semantic models, from taxonomists and ontologists, to software engineers and data architects. Most of us have had to deal with the fallouts from bad modeling decisions. Panos’ clear-sighted text offers practical guidance and pragmatic advice to help you avoid the traps of vague, misleading, and just plain wrong semantic modeling.

Helen Lippel

Among the attempts to bring logic, ontology, and semiotics in information engineering, this book is probably one the best and more complete sources.

Guido Vetere

Panos really covers most of the ground of modeling concerns and he really means “for Data”. The book sort of closes the gap between old school semantics and old school Data Modeling. It applies to graph data models and also to other contexts.

Thomas Frisendal

Outstanding, especially with the focus on how to think about the problem (i.e., what’s needed for implementations) instead of tools.

Paco Nathan

About the author

Panos Alexopoulos has been working since 2006 at the intersection of data, semantics, and software, contributing to 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.

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Semantic Talks

Videos and slides related to the book

Crafting a Knowledge Graph Strategy @ CDL 2018

Developing Knowledge Graphs in Organizations @ DIS 2018

Building Knowledge Graphs in the Real World @ CDL 2018