The role of vocabularies on the Semantic Web are to help data integration when, for example, ambiguities may exist on the terms used in the different data sets, or when a bit of extra knowledge may lead to the discovery of new relationships. Consider, for example, the application of ontologies in the field of health care. Medical professionals use them to represent knowledge about symptoms, diseases, and treatments. Pharmaceutical companies use them to represent information about drugs, dosages, and allergies. Combining this knowledge from the medical and pharmaceutical communities with patient data enables a whole range of intelligent applications such as decision support tools that search for possible treatments; systems that monitor drug efficacy and possible side effects; and tools that support epidemiological research.

Another type of example is to use vocabularies to organize knowledge. Libraries, museums, newspapers, government portals, enterprises, social networking applications, and other communities that manage large collections of books, historical artifacts, news reports, business glossaries, blog entries, and other items can now use vocabularies, using standard formalisms, to leverage the power of linked data.

It depends on the application how complex vocabularies they use. Some applications may decide not to use even small vocabularies, and rely on the logic of the application program. Some application may choose to use very simple vocabularies like the one described in the examples section below, and let a general Semantic Web environment use that extra information to make the identification of the terms. Some applications need an agreement on common terminologies, without any rigor imposed by a logic system. Finally, some applications may need more complex ontologies with complex reasoning procedures. It all depends on the requirements and the goals of the applications.

To satisfy these different needs, W3C offers a large palette of techniques to describe and define different forms of vocabularies in a standard format. These include RDF and RDF Schemas, Simple Knowledge Organization System (SKOS), Web Ontology Language (OWL), and the Rule Interchange Format (RIF). The choice among these different technologies depend on the complexity and rigor required by a specific application.

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