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Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas
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.
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
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.
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
- GenresProgrammingTechnology
330 pages, Kindle Edition
Published August 19, 2020
31 people are currently reading
106 people want to read
About the author
Panos Alexopoulos
5 books7 followersPanos 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.
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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Displaying 1 - 2 of 2 reviews
January 31, 2022
If you liked Data & Reality, you'll probably like this book. But make no mistake, this book isn't some sort of introduction to or a detailed how-to guide for semantic modelling and knowledge graph building. I enjoyed this book because I already had my share of semantic (web) and knowledge graph modeling such as using AllegroGraph RDF database for building a recommendation system, using TopBraid Composer to design OWL ontologies for some space industry projects, and build multi-lingual search systems making use of DBpedia and other semantic web systems.
If you also tackled some semantic web or knowledge graph challenges with respect to modeling a domain, or even if you tried to build a complex and nuanced relational database model for an involved domain, I believe you will appreciate many parts of the book. Not only because you'll be able to relate to it, but also because some of the insights by the author will probably help you with your modeling challenges.
For me, it was also nice to see that the author tried to build some connections to modern machine learning and deep learning approaches. I haven't seen this kind of approach in other, more technical books, and I think with the recent advances in deep learning based approaches to natural language processing, it is inevitable that semantic modeling and knowledge graph building efforts will need to take that into account.
Some readers will probably find some of the finer discussions "philosophical", but there's no other way: if your job is to model "reality" using some formalism, you are bound to deal with some parts of philosophy. Of course, in this book, no matter how technical the discussion gets, the author manages to keep a pragmatic perspective and always tie things to some concrete business problems he encountered in his semantic modeling projects, something that should be really appreciated.
Long story short, if you want to learn the basics of some concrete semantic modelling technologies, go read Learning SPARQL or Semantic Web for the Working Ontologist: Effective Modeling for Linked Data, Rdfs, and Owl (and Neo4j documentation). If you've already done that and busy with some modeling challenges, then read this book.
Oh, by the way, be careful with DBPedia, too! ;)
If you also tackled some semantic web or knowledge graph challenges with respect to modeling a domain, or even if you tried to build a complex and nuanced relational database model for an involved domain, I believe you will appreciate many parts of the book. Not only because you'll be able to relate to it, but also because some of the insights by the author will probably help you with your modeling challenges.
For me, it was also nice to see that the author tried to build some connections to modern machine learning and deep learning approaches. I haven't seen this kind of approach in other, more technical books, and I think with the recent advances in deep learning based approaches to natural language processing, it is inevitable that semantic modeling and knowledge graph building efforts will need to take that into account.
Some readers will probably find some of the finer discussions "philosophical", but there's no other way: if your job is to model "reality" using some formalism, you are bound to deal with some parts of philosophy. Of course, in this book, no matter how technical the discussion gets, the author manages to keep a pragmatic perspective and always tie things to some concrete business problems he encountered in his semantic modeling projects, something that should be really appreciated.
Long story short, if you want to learn the basics of some concrete semantic modelling technologies, go read Learning SPARQL or Semantic Web for the Working Ontologist: Effective Modeling for Linked Data, Rdfs, and Owl (and Neo4j documentation). If you've already done that and busy with some modeling challenges, then read this book.
Oh, by the way, be careful with DBPedia, too! ;)
November 17, 2022
This book won't help you on practical data modelling. It is structured about listing things, usually things that can go wrong, and having done some semantic modelling myself in the past, I'm glad my struggles weren't so uncommon. So basically it is kind of a retrospective of what things that can go wrong. In a sense is like a war diary that for people knowledgeable in the topic can be of some help.
But if you are (as I was) looking for someone giving you an updated approach on:
- Picking the right DB for your problem
- Picking the right language for describing your ontology
- Defining a methodological approach for Ontological engineering
- Presenting modern upper or medium level ontologies you could use
- Helping you on how to setup algorithms for Information Extraction and Disambiguation
- Ingesting and/or presenting semantic data
this is not the book for you (and I'm still looking for one). Although all those topics are mentioned in the book, you never know if you should follow the topic or not.
The author himself states at the end of the book:
But then if that would be the case, no software engineering book would have ever been written.
But if you are (as I was) looking for someone giving you an updated approach on:
- Picking the right DB for your problem
- Picking the right language for describing your ontology
- Defining a methodological approach for Ontological engineering
- Presenting modern upper or medium level ontologies you could use
- Helping you on how to setup algorithms for Information Extraction and Disambiguation
- Ingesting and/or presenting semantic data
this is not the book for you (and I'm still looking for one). Although all those topics are mentioned in the book, you never know if you should follow the topic or not.
The author himself states at the end of the book:
The main reason why I have structured the book around pitfalls and dilemmas and haven’t given you a set of recipes for building the perfect semantic model is that I have no idea what such a model looks like for your domain, data, and application context. In other words, my map does not necessarily reflect your territory.
But then if that would be the case, no software engineering book would have ever been written.
Displaying 1 - 2 of 2 reviews