The key goal of the Semantic Web is to trigger the evolution of the existing Web to enable users to search, discover, share and join information with less effort.
Humans can use the Web to execute multiple tasks, such as booking online tickets, searching for different information, using online dictionaries, etc.
The term was coined by Tim Berners-Lee for a web of data (or data web) that can be processed by machines —that is, one in which much of the meaning is machine-readable.
While its critics have questioned its feasibility, proponents argue that applications in library and information science, industry, biology and human sciences research have already proven the validity of the original concept.
Berners-Lee originally expressed his 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 makes 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. The 2001 Scientific American article by Berners-Lee, Hendler, and Lassila described an expected evolution of the existing Web to a Semantic Web.
In 2006, Berners-Lee and colleagues stated that: “This simple idea…remains largely unrealized”.In 2013, more than four million Web domains contained Semantic Web markup.
Even so, machines are not able to carry out any of these tasks without human intervention because Web pages are made to be read by humans, not machines.
The Semantic Web can be considered a vision for the future in which data could be quickly interpreted by machines, allowing them to carry out numerous tedious tasks related to discovering, blending, and taking action on the information available on the Web.
The Semantic Web is a process that allows machines to quickly understand and react to complicated human requests subject to their meaning. This kind of understanding mandates that the appropriate information sources are semantically structured, which is a difficult task.
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