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Preface to the second edition
Since the first edition of Semantic Web for the Working Ontologist came out in June 2008, we have been encouraged by the reception the book has received. Practitioners from a wide variety of industries—health care, energy, environmental science, life sciences, national intelligence, and publishing, to name a few—have told us that the first edition clarified for them the possibilities and capabilities of Semantic Web technology. This was the audience we had hoped to reach, and we are happy to see that we have.
Since that time, the technology standards of the Semantic Web have continued to develop. SPARQL, the query language for RDF, became a Recommendation from the World Wide Web Consortium and was so successful that version 2 is already nearly ready (it will probably be ratified by the time this book sees print). SKOS, which we described as an example of modeling “in the wild” in the first edition, has raced to the forefront of the Semantic Web with high-profile uses in a wide variety of industries, so we gave it a chapter of its own. Version 2 of the Web Ontology Language, OWL, also appeared during this time.
Probably the biggest development in the Semantic Web standards since the first edition is the rise of the query language SPARQL. Beyond being a query language, SPARQL is a powerful graph-matching language which pushes its utility beyond simple queries. In particular, SPARQL can be used to specify general inferencing in a concise and precise way. We have adopted it as the main expository language for describing inferencing in this book. It turns out to be a lot easier to describe RDF, RDFS, and OWL in terms of SPARQL.
The “in the wild” sections became problematic in the second edition, but for a good reason—we had too many good examples to choose from. We're very happy with the final choices, and are pleased with the resulting “in the wild” chapters (9 and 13). The Open Graph Protocol and Good Relations are probably responsible for more serious RDF data on the Web than any other efforts. While one may argue (and many have) that FOAF is getting a bit long in the tooth, recent developments in social networking have brought concerns about privacy and ownership of social data to the fore; it was exactly these concerns that motivated FOAF over a decade ago. We also include two scientific examples of models “in the wild”—QUDT (Quantities, Units, Dimensions, and Types) and The Open Biological and Biomedical Ontologies (OBO). QUDT is a great example of how SPARQL can be used to specify detailed computation over a large set of rules (rules for converting units and for performing dimensional analysis). The wealth of information in the OBO has made them perennial favorites in health care and the life sciences. In our presentation, we hope to make them accessible to an audience who doesn't have specialized experience with OBO publication conventions. While these chapters logically build on the material that precedes them, we have done our best to make them stand alone, so that impatient readers who haven't yet mastered all the fine points of the earlier chapters can still appreciate the “wild” examples.
We have added some organizational aids to the book since the first edition. The “Challenges” that appear throughout the book, as in the first edition, provide examples for how to use the Semantic Web technologies to solve common modeling problems. The “FAQ” section organizes the challenges by topic, or, more properly, by the task that they illustrate. We have added a numeric index of all the challenges to help the reader cross-reference them.
We hope that the second edition will strike a chord with our readers as the first edition has done.
On a sad note, many of the examples in Chapter 5 use “Elizabeth Taylor” as an example of a “living actress.” During postproduction of this book, Dame Elizabeth Taylor succumbed to congestive heart failure and died. We were too far along in the production to make the change, so we have kept the examples as they are. May her soul rest in peace.
Preface to the first edition
In 2003, when the World Wide Web Consortium was working toward the ratification of the Recommendations for the Semantic Web languages, RDF, RDFS, and OWL, we realized that there was a need for an industrial-level introductory course in these technologies. The standards were technically sound, but, as is typically the case with standards documents, they were written with technical completeness in mind rather than education. We realized that for this technology to take off, people other than mathematicians and logicians would have to learn the basics of semantic modeling.
Toward that end, we started a collaboration to create a series of trainings aimed not at university students or technologists but at Web developers who were practitioners in some other field. In short, we needed to get the Semantic Web out of the hands of the logicians and Web technologists, whose job had been to build a consistent and robust infrastructure, and into the hands of the practitioners who were to build the Semantic Web. The Web didn't grow to the size it is today through the efforts of only HTML designers, nor would the Semantic Web grow as a result of only logicians' efforts.
After a year or so of offering training to a variety of audiences, we delivered a training course at the National Agriculture Library of the U.S. Department of Agriculture. Present for this training were a wide variety of practitioners in many fields, including health care, finance, engineering, national intelligence, and enterprise architecture. The unique synergy of these varied practitioners resulted in a dynamic four-day investigation into the power and subtlety of semantic modeling. Although the practitioners in the room were innovative and intelligent, we found that even for these early adopters, some of the new ways of thinking required for modeling in a World Wide Web context were too subtle to master after just a one-week course. One participant had registered for the course multiple times, insisting that something else “clicked” each time she went through the exercises.
This is when we realized that although the course was doing a good job of disseminating the information and skills for the Semantic Web, another, more archival resource was needed. We had to create something that students could work with on their own and could consult when they had questions. This was the point at which the idea of a book on modeling in the Semantic Web was conceived. We realized that the readership needed to include a wide variety of people from a number of fields, not just programmers or Web application developers but all the people from different fields who were struggling to understand how to use the new Web languages.
It was tempting at first to design this book to be the definitive statement on the Semantic Web vision, or “everything you ever wanted to know about OWL,” including comparisons to program modeling languages such as UML, knowledge modeling languages, theories of inferencing and logic, details of the Web infrastructure (URIs and URLs), and the exact current status of all the developing standards (including SPARQL, GRDDL, RDFa, and the new OWL 1.1 effort). We realized, however, that not only would such a book be a superhuman undertaking, but it would also fail to serve our primary purpose of putting the tools of the Semantic Web into the hands of a generation of intelligent practitioners who could build real applications. For this reason, we concentrated on a particular essential skill for constructing the Semantic Web: building useful and reusable models in the World Wide Web setting.
Many of these patterns entail several variants, each embodying a different philosophy or approach to modeling. For advanced cases such as these, we realized that we couldn't hope to provide a single, definitive answer to how these things should be modeled. So instead, our goal is to educate domain practitioners so that they can read and understand design patterns of this sort and have the intellectual tools to make considered decisions about which ones to use and how to adapt them. We wanted to focus on those trying to use RDF, RDFS, and OWL to accomplish specific tasks and model their own data and domains, rather than write a generic book on ontology development. Thus, we have focused on the “working ontologist” who was trying to create a domain model on the Semantic Web.
The design patterns we use in this book tend to be much simpler. Often a pattern consists of only a single statement but one that is especially helpful when used in a particular context. The value of the pattern isn't so much in the complexity of its realization but in the awareness of the sort of situation in which it can be used.
This “make it useful” philosophy also motivated the choice of the examples we use to illustrate these patterns in this book. There are a number of competing criteria for good example domains in a book of this sort. The examples must be understandable to a wide variety of audiences, fairly compelling, yet complex enough to reflect real modeling situations. The actual examples we have encountered in our customer modeling situations satisfy the last condition but either are too specialized—for example, modeling complex molecular biological data; or, in some cases, they are too business-sensitive—for example, modeling particular investment policies—to publish for a general audience.
We also had to struggle with a tension between the coherence of the...