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It has long been realized that the web could benefit from having its content understandable and available in a machine processable form. The Semantic Web aims to achieve this via annotations that use terms defined in ontologies to give well defined meaning to web accessible information and services. OWL, the ontology language recommended by the W3C for this purpose, was heavily influenced by Description Logic research. In this chapter we review briefly some early efforts that combine Description Logics and the web, including predecessors of OWL such as OIL and DAML+OIL. We then go on to describe OWL in some detail, including the various influences on its design, its relationship with RDFS, its syntax and semantics, and a range of tools and applications.
Background and history
The World Wide Web, while wildly successful in growth, may be viewed as being limited by its reliance on languages such as HTML that are focused on presentation (i.e., text formatting) rather than content. Languages such as XML do add some support for capturing the meaning of web content (instead of simply how to render it in a browser), but more is needed in order to support intelligent applications that can better exploit the ever increasing range of information and services accessible via the web. Such applications are urgently needed in order to avoid overwhelming users with the sheer volume of information becoming available.
A DL-based knowledge representation system is more than an inference engine for a particular Description Logic. A knowledge representation system must provide a number of services to human users, including presentation of the information stored in the system in a manner palatable to users and justification of the inferences performed by the system. If human users cannot understand what the system is doing, then the development of knowledge bases is made much more difficult or even impossible. A knowledge representation system must also provide a number of services to application programs, including access to the basic information stored in the system but also including access to the machinations of the system. If programs cannot easily access and manipulate the information stored in the system, then the development of applications is made much more difficult or even impossible.
A DL-based knowledge representation system does not live in a vacuum. It has to be prepared to interact with several sorts of other entities. One class of entities consists of human users who develop knowledge bases using the system. If the system cannot effectively interact with these users then it will be difficult to create knowledge bases in the system, and the system will not be used. Another class of entities consists of programs that use the services of the system to provide information to support applications. If the system cannot effectively interact with these programs then it will be difficult to create applications using the system, and the system will not be used.
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