Machine-processable representations of ontologies will be crucial to many applications of the semantic Web. They are unable to find relevant information that is described in different terms, they often return information that uses the same words with a different meaning, and they are unable to combine information from multiple sources. These problems can be alleviated if search engines no longer search for matching keywords, but search on the semantic concepts that underly the information in web-pages. Traditional document-management systems provide insufficient means to structure and access the knowledge in such a repository. Explicit ontologies are the most promising technical vehicle for transforming document repositories into proper knowledge repositories.
Ontology editors help human knowledge engineers to develop and maintain ontologies.
They support the definition and modification of concepts, slots, axioms and constraints, as well as enabling the inspection, browsing and codifying of the resulting ontologies. Inference engines can be used to reason about ontologies, helping both to build them and to use them for advanced information access and navigation. FaCT is heavily optimised to deal with very large ontologies. The above example was stated in OIL's presentation syntax, which is intended for human readers and writers of OIL ontologies. For machines, OIL uses RDF as its syntax.