July 2013
Beginner
386 pages
10h 33m
English
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Chapter 9. RDF Schema, OWL, and Inferencing
In Chapter 2, we learned the basics of the roles that RDF Schema (RDFS) and the Web Ontology Language (OWL) can play in RDF applications:
RDFS and OWL are W3C standard vocabularies that let you define and describe classes and properties that a dataset’s triples might use. These do not function as templates that the data must conform to, as schemas often do in other data modeling systems, but instead as additional metadata to help you get more out of your data.
RDFS and OWL statements themselves are expressed using triples, so you can query them with SPARQL.
Properties and classes from the RDFS and OWL vocabularies let you describe your own properties and classes in ways that let certain applications infer new information from your dataset. For example, if you have one triple that specifies that the
ab:spouseproperty is anowl:SymmetricProperty, and you have another triple that tells us that Richard has anab:spousevalue of Cindy, then an application that understands whatowl:SymmetricPropertymeans will know that Cindy has anab:spousevalue of Richard. This is a classic example of the value of metadata: it adds information about your data so that you can get more out of it.
What kind of applications understand what owl:SymmetricProperty means, or what RDFS
properties such as rdfs:domain and rdfs:range mean? And how does this extend the power of what you can do with your SPARQL queries? We’ll learn the answers to these questions in this ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.