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 SemanticWeb have continued to develop. SPARQL,
the query language for RDF, became a Recommendation from theWorldWideWeb 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 SemanticWeb 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 seriousRDF data on theWeb 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 tomake 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.
