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Semantic Web for the Working Ontologist: Effective Modeling for Linked Data, RDFS, and OWL
Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.
The Semantic Web treats data as a distributed resource on the scale of the World Wide Web, and incorporates features to address the challenges of massive data distribution as part of its basic design. The aim of the first two editions was to motivate the Semantic Web technology stack from end-to-end; to describe not only what the Semantic Web standards are and how they work, but also what their goals are and why they were designed as they are. It tells a coherent story from beginning to end of how the standards work to manage a world-wide distributed web of knowledge in a meaningful way.
The third edition builds on this foundation to bring Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, bringing with him years of experience in global linked data, to open up the story to a modern view of global linked data. While the overall story is the same, the examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data. Also included with the third edition, all of the data sets and queries are available online for study and experimentation at data.world/swwo.
The Semantic Web treats data as a distributed resource on the scale of the World Wide Web, and incorporates features to address the challenges of massive data distribution as part of its basic design. The aim of the first two editions was to motivate the Semantic Web technology stack from end-to-end; to describe not only what the Semantic Web standards are and how they work, but also what their goals are and why they were designed as they are. It tells a coherent story from beginning to end of how the standards work to manage a world-wide distributed web of knowledge in a meaningful way.
The third edition builds on this foundation to bring Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, bringing with him years of experience in global linked data, to open up the story to a modern view of global linked data. While the overall story is the same, the examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data. Also included with the third edition, all of the data sets and queries are available online for study and experimentation at data.world/swwo.
498 pages, Kindle Edition
Published August 3, 2020
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Displaying 1 - 3 of 3 reviews
February 1, 2023
If you're looking to develop a firm understanding of what The Semantic Web is and learn great ontology modeling practices, this book is definitely for you.
It's very well structured, formatted and written. I think the authors did particularly well on the educational side: clear explanations, great anticipation on what readers might be wondering, examples usually supported in both text and visual format, andsoforth.
After been given a great introduction chapters 1 and 2 of The Semantic Web and semantlc modeling, chapter 3 goes on to explain RDF, which is a really comprehensive and educational chapter. Of the few times my mind was blown in this book, section 3.5 was the first one to do it by dissecting a table in all possible ways (by row, column and cell), illuminating the pros and cons, and how this relates to RDF.
Chapter 4 goes in-depth on the Web architecture on which The Semantic Web is built, particularly what an application landscape can looke like. Chapter 5 then continues this discussion of underlying Web mechanisms (such as HTTP and URIs), but in the context of Linked Data. It does a good job in particular to explain how to deal with the difference between "a cat" as a thing itself, and the "document of a cat" that describes it.
The following chapter, chapter 6, is probably the biggest and most hands-on in the book. It teaches you how to query and construct triples with SPARQL. There's not much to say about it, other than that it's quite comprehensive and does a good job getting you acquainted with the language.
Chapter 7 is one of my favorite chapters. It explains the foundational difference between "inference" and "expectation", how each plays a major role in data management, and how The Semantic Web relates to these concepts. The idea of inferencing in particular is really crucial to understand if you're going to work with RDFS and OWL in an open world. As a software developer I especially had to adapt to this kind of thinking, since then my data(model) operates within a closed world and therefore has quite different assumptions underlying them. It in this chapter (and in part in the previous) that I had my mind blown for the second time, upon learning that SPARQL rules can be used to express the formal meaning of resources.
From chapter 8 onwards, the book starts to focus a lot on conceptual modeling. It starts with introducing the simple RDF Schema (RDFS), where I really appreciated the section on modeling with domains and ranges. Also, potential pitfalls with regards to inferencing are discussed. Finally, it's interesting to note that chapters 7 and 8 both contain some sidebars that explain the differences between object orientation in software development and ontology design. As a programmer I found those to be really illuminating.
Chapter 9 that describes the subset of OWL that they call RDFS+ is probably one of the most important chapters from a practial viewpoint. The concepts you learn here will get you very far as an ontologist, including functional and inverse functional properties, class equivalence, inverses, etc. Chapter 10 goes on to show examples of large and famous projects that use the modeling tools.
Chapter 11 discusses SKOS, a language to manage vocabularies/thesauri. The main point they stretch is that, different from RDFS/OWL, SKOS is not about meaning and inference, but about the terms themselves. Concepts can be said to be smaller or broader in meaning than others, for example. Again: there is no formal meaning behind this. That's not the point of SKOS.
Starting with chapter 12, the difficulty ramps up considerably. It's very manageable thanks to the clear writing, but it deservers a mention nonetheless. Chapters 12 and 13 are also very interesting, so it's worth it!
These two chapters explain restriction classes based on values (chapter 12) and cardinalities (chapter 13). The kinds of restrictions allow you to express complex types of classes that are restricted in terms of what members are part of it. It's really closely related to the concept of set comprehensions in mathematics. Sometimes it's even trivial how to map both onto each other. A particular shout-out goes to the Class-Indivual Mirror pattern, a pattern that enables you to basically "switch perspective" between class and individual for a concept. Chapter 13 also discusses the challenges with counting under the Open World Assumption which is really quite fascinating. How do you when you've satisfied a cardinality constraint? Perhaps there's other individuals to count that we don't know about yet. Finally, the subtlety of contradictions in OWL models is also explained.
Chapter 14 is another of those that show you existing projects that utilize the concepts you've learned. This chapter was the hardest to get through for me, in part because I feel it simply was rather difficult, but also because some crucial things seemed unexplained. Also, there was more confusion and sloppiness in naming things and such. On the other hand, it does provide a good introduction in QUDT, OBO and FIBO.
Chapter 15 addresses some good and bad modeling practices. How to get started depending on the situation you're in, what are good naming practices and when do they apply, but most interestingly they discuss anti-patterns. This section is very interesting, especially the "class vs. individual" theme that some anti-patterns share. As a programmer, I should probably watch out for the "exclusivity" and "objectification" anti-patterns in particular.
Chapter 16 touches briefly on advanced modeling in OWL 2. Mostly it lays out the four subsets it is divided into, discussing their purposes and use cases.
The book ends with a wrapping up Chapter 17 about which I have nothing noteworthy to say.
Although it gets a little sloppier at the end, like many technical books seem to do, this book is fantastic. It packs a whole bunch of topics and perspectives and the authors have managed to present it coherently and with a consistent and good pace. For a very decent overview and head start this book is perfect. For those who are looking for technical details and advanced subtleties you're probably going to have much of your questions unanswered. Don't let this statement underestimate the thoroughness of the book though. It may be suited mostly for beginners/novices, but it really does train you quite deeply.
It's very well structured, formatted and written. I think the authors did particularly well on the educational side: clear explanations, great anticipation on what readers might be wondering, examples usually supported in both text and visual format, andsoforth.
After been given a great introduction chapters 1 and 2 of The Semantic Web and semantlc modeling, chapter 3 goes on to explain RDF, which is a really comprehensive and educational chapter. Of the few times my mind was blown in this book, section 3.5 was the first one to do it by dissecting a table in all possible ways (by row, column and cell), illuminating the pros and cons, and how this relates to RDF.
Chapter 4 goes in-depth on the Web architecture on which The Semantic Web is built, particularly what an application landscape can looke like. Chapter 5 then continues this discussion of underlying Web mechanisms (such as HTTP and URIs), but in the context of Linked Data. It does a good job in particular to explain how to deal with the difference between "a cat" as a thing itself, and the "document of a cat" that describes it.
The following chapter, chapter 6, is probably the biggest and most hands-on in the book. It teaches you how to query and construct triples with SPARQL. There's not much to say about it, other than that it's quite comprehensive and does a good job getting you acquainted with the language.
Chapter 7 is one of my favorite chapters. It explains the foundational difference between "inference" and "expectation", how each plays a major role in data management, and how The Semantic Web relates to these concepts. The idea of inferencing in particular is really crucial to understand if you're going to work with RDFS and OWL in an open world. As a software developer I especially had to adapt to this kind of thinking, since then my data(model) operates within a closed world and therefore has quite different assumptions underlying them. It in this chapter (and in part in the previous) that I had my mind blown for the second time, upon learning that SPARQL rules can be used to express the formal meaning of resources.
From chapter 8 onwards, the book starts to focus a lot on conceptual modeling. It starts with introducing the simple RDF Schema (RDFS), where I really appreciated the section on modeling with domains and ranges. Also, potential pitfalls with regards to inferencing are discussed. Finally, it's interesting to note that chapters 7 and 8 both contain some sidebars that explain the differences between object orientation in software development and ontology design. As a programmer I found those to be really illuminating.
Chapter 9 that describes the subset of OWL that they call RDFS+ is probably one of the most important chapters from a practial viewpoint. The concepts you learn here will get you very far as an ontologist, including functional and inverse functional properties, class equivalence, inverses, etc. Chapter 10 goes on to show examples of large and famous projects that use the modeling tools.
Chapter 11 discusses SKOS, a language to manage vocabularies/thesauri. The main point they stretch is that, different from RDFS/OWL, SKOS is not about meaning and inference, but about the terms themselves. Concepts can be said to be smaller or broader in meaning than others, for example. Again: there is no formal meaning behind this. That's not the point of SKOS.
Starting with chapter 12, the difficulty ramps up considerably. It's very manageable thanks to the clear writing, but it deservers a mention nonetheless. Chapters 12 and 13 are also very interesting, so it's worth it!
These two chapters explain restriction classes based on values (chapter 12) and cardinalities (chapter 13). The kinds of restrictions allow you to express complex types of classes that are restricted in terms of what members are part of it. It's really closely related to the concept of set comprehensions in mathematics. Sometimes it's even trivial how to map both onto each other. A particular shout-out goes to the Class-Indivual Mirror pattern, a pattern that enables you to basically "switch perspective" between class and individual for a concept. Chapter 13 also discusses the challenges with counting under the Open World Assumption which is really quite fascinating. How do you when you've satisfied a cardinality constraint? Perhaps there's other individuals to count that we don't know about yet. Finally, the subtlety of contradictions in OWL models is also explained.
Chapter 14 is another of those that show you existing projects that utilize the concepts you've learned. This chapter was the hardest to get through for me, in part because I feel it simply was rather difficult, but also because some crucial things seemed unexplained. Also, there was more confusion and sloppiness in naming things and such. On the other hand, it does provide a good introduction in QUDT, OBO and FIBO.
Chapter 15 addresses some good and bad modeling practices. How to get started depending on the situation you're in, what are good naming practices and when do they apply, but most interestingly they discuss anti-patterns. This section is very interesting, especially the "class vs. individual" theme that some anti-patterns share. As a programmer, I should probably watch out for the "exclusivity" and "objectification" anti-patterns in particular.
Chapter 16 touches briefly on advanced modeling in OWL 2. Mostly it lays out the four subsets it is divided into, discussing their purposes and use cases.
The book ends with a wrapping up Chapter 17 about which I have nothing noteworthy to say.
Although it gets a little sloppier at the end, like many technical books seem to do, this book is fantastic. It packs a whole bunch of topics and perspectives and the authors have managed to present it coherently and with a consistent and good pace. For a very decent overview and head start this book is perfect. For those who are looking for technical details and advanced subtleties you're probably going to have much of your questions unanswered. Don't let this statement underestimate the thoroughness of the book though. It may be suited mostly for beginners/novices, but it really does train you quite deeply.
August 7, 2021
Excellent book for understanding Semantic Web, has some concrete examples too.
Read
November 9, 2024Read 4 chapters.
Putting this book on hold until it becomes relevant again.
Putting this book on hold until it becomes relevant again.
Displaying 1 - 3 of 3 reviews