KNOWLEDGE REPRESENTATION & THE SEMANTIC WEB
The Semantic Web is the name given to an evolving extension of the World Wide Web. The design and development of this Semantic Web was initiated by Tim Berners-Lee, the inventor of the World Wide Web, and the director of the World Wide Web Consortium, W3C. In the Semantic Web, the meaning of information and services on the web will be defined and therefore making it possible for the web to understand and satisfy the needs and requests of the people or machines to use the web content, a somewhat artificial intelligence.
For example humans can carry out tasks on the Web such as searching for the cheapest DVD and then buy it. On the other hand a computer cannot accomplish the same tasks without our direction because web pages are not designed to be read by machines. The semantic web is a vision of information that is understandable by computers, so that the machine itself can perform some of the dreary work involved in searching, sharing and combining information on the web.
Tim Berners-Lee originally expressed in 1999, the vision of the semantic web as follows:
“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.“
For the semantic web to function, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. This is where knowledge representation comes in. Traditional knowledge representation systems typically have been centralised requiring all users to share exactly the same definition of common concepts. But central control is overpowering, and increasing the size and scope of such a system so quickly becomes uncontrollable.
The problem with knowledge representation systems is that they limit the questions that can be asked, and be reliably answered. To avoid such problems, traditional knowledge representation systems each have their own set of rules for making inferences about their data. For example, a system acting on a database of birds, might include the rule “all birds have wings”. Even if the data could be transferred from one system to another, the rules usually could not.