Profiling Semantic Web (Part I)
I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize.
Tim Berners Lee, 1999
Semantic Web is a group of methods and technologies to allow machines to understand the meaning – or “semantics” – of information on the World Wide Web.
Humans are capable of using the Web to carry out tasks such as finding the Irish word for “directory,” reserving a library book, and searching for a low price for a DVD. However, a computer cannot accomplish all of these tasks without human direction, because web pages are designed to be read by people, not machines. The semantic web is a vision of information that is understandable by computers, so computers can perform more of the tedious work involved in finding, combining, and acting upon information on the web. This will be accomplished by the availability of machine-readable metadata that would enable automated agents and other software to access the Web more intelligently. The agents would be able to perform tasks automatically and locate related information on behalf of the user.
These technologies include a variety of data interchange formats, notations and the Web Ontology Language, all of which are intended to provide a formal description of concepts, terms, and relationships within a given knowledge domain. These technologies are combined in order to provide descriptions that supplement or replace the content of Web documents. Thus, content may manifest itself as descriptive data stored in Web-accessible databases, or as markup within documents, XHML and XML. The machine-readable descriptions enable content managers to add meaning to the content, i.e., to describe the structure of the knowledge we have about that content. In this way, a machine can process knowledge itself, instead of text, using processes similar to human deductive reasoning and inference, thereby obtaining more meaningful results and helping computers to perform automated information gathering and research.