When the average person needs information, they use a search engine. The phrase “Google it” has become mainstream, which just shows how much we rely on search engines for information.
We enter a query into a search engine, different sites come up and we go to the one that has the information we need. And we are able to understand that information, because it is presented in a way we are able to understand.
Not only is the information written in a language we can understand, but it is also formatted and structured in a way we are used to seeing.
Machine Learning
Now think about how a machine would gather that same information. Simply because humans are able to understand information the way it is formatted on web pages, does not mean machines can do the same. And that is where the semantic web comes into the picture.
Semantic Web
The purpose of the semantic web is to create an internet where machines are able to access and understand the information that exists on the world wide web. Machines should be able to understand that information, correct it if it is incorrect and determine whether that information is coming from an authoritative source.
When we think about machines doing such processes, our mind goes to digital assistants such as Siri, Alexa, Bixby or Google Assistant. The semantic web is a much wider ranging concept that would build on such digital assistants.
In a single sentence, the semantic web is a web of data that is directly and indirectly accessible to and processable by machines. It is a concept that has been around for many decades, before the internet even came into being.
Not only would the semantic web ensure that machines have an easier time finding complex information on the web, but the accuracy and speed of their information gathering would go up.