Universal implementation of the Semantic Web may still be years away, but nonetheless many web departments are increasingly adopting semantic standards and migrating to semantic technology-based products to take advantage of the benefits the Semantic Web has to offer.
Take for example OWL, the Web Ontology Language increasingly used by applications that go beyond just presenting information to humans and actually process the content of that information. OWL is superbly designed for those tasks, facilitating greater machine interpretability of Web content than any of its predecessors (XML, RDF, RDF Schema) by providing additional vocabulary along with a formal semantics.
Ontology in itself is a very slippery concept and it has been so for many centuries. It could be defined as the philosophical study of the nature of being, as well as the basic categories of being and their relations. In computing environments ontologies refer to the structural frameworks for organizing information, and besides the Semantic Web, are commonly used in artificial intelligence, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, information architecture, etc. The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture framework.
The Semantic Web is a vision for the future of the Web in which information is given explicit meaning, making it easier for machines to automatically process and integrate information available on the Web. The Semantic Web will build on XML’s ability to define customized tagging schemes and RDF’s flexible approach to representing data. The first level above RDF required for the Semantic Web is an ontology language what can formally describe the meaning of terminology used in Web documents. If machines are expected to perform useful reasoning tasks on these documents, the language must go beyond the basic semantics of RDF Schema. The OWL Use Cases and Requirements Document provides more details on ontologies, motivates the need for a Web Ontology Language, and formulates design goals, requirements and objectives for OWL.
The primary goals of the Web Ontology Language are to meet the growing stack of W3C recommendations related to the Semantic Web. It is an evolved version of a set of technologies such as XML, that provides a surface syntax for structured documents, but imposes no semantic constraints on the meaning of these documents, XML Schema, a language for restricting the structure of XML documents and also extends XML with datatypes, RDF, a datamodel for objects and relations between them, that provides a simple semantics for this datamodel that in turn can be represented in an XML syntax, RDF Schema, a vocabulary for describing properties and classes of RDF resources, with a semantics for generalization-hierarchies of such properties and classes.
OWL adds more vocabulary for describing properties and classes: among others, relations between classes (e.g. disjointness), cardinality (e.g. “exactly one”), equality, richer typing of properties, characteristics of properties (e.g. symmetry), and enumerated classes.
That’s the theory. In practice, seven years on from OWL becoming a W3C recommendation, and two years on from the more recent OWL 2 W3C recommendation, OWL has still experienced only patchy uptake on the Web. Although certain OWL features are very popular, many other features are largely neglected by publishers. This may suggest that despite the promise of easy implementations and the proposal of tractable profiles suggested in OWL’s second version, there is still no “right” standard fragment for the Linked Data community. But even if OWL as a whole might never arrive on the Web of Data, the OWL LD profile is a natural fit for ontological modelling on the Web of Data