Semantic Web and Ontological Modelling Introduction

Semantic Web and Ontological Modelling is an interesting area of research. This is also the base for knowledge bases in data mining and warehousing too. Moreover, it is used in creating web intelligence and agent systems. Before going to deep areas of semantic web and ontology let us get the basic idea.

In this article, we will discuss the limits of the web. This is discussed by dividing the are into 3 generations. The importance of the meaning of these words will also be discussed. Web content understanding will be explained with an example. Furthermore, we will discuss the semantic web and web of data. And how to use them too.  So keep reading the article for more detailed information.

Limits of the web in Semantic Web and Ontological Modelling

Before discussing the Semantic Web and Ontological Modelling concepts we should know what the web is. the Web can be identified as a network of networks which consists of virtual knowledge.

First Generation

The first generation of the web was called the internet. The internet was basically computer-centered processing. The information was taken as follows.

1.    We should open the terminal.
2.    Then we need to connect to the remote system where the necessary data is located
3.    Then we should retrieve the data file from the correct system
4.    Download the retrieved data to the local system
5.    Them we can read the necessary information

But, there were problems related to the first generation internet. The main problem was the process is expensive. Information retrieval was a huge expense. And also to retrieve the information an expert knowledge was needed. Not everyone could access the information when they need. It had a specific available time period and a certain capacity of retrieval too.

Second Generation

In Semantic Web and Ontological Modelling second generation started calling the web. And this was a document centered processing. As we all know to get the information we need we can simply open the browser. Then load the web document. Then we can use hyper links to surf through the web. This has many advantages. Such as we do not need any expert knowledge to get data from the web. Anyone had access to the web. And the information was retrieved via search engines. So that we did not need prior knowledge where the data was located. It was an easy task.

According to Douglas Adams – in Semantic Web and Ontological Modeling he has mentioned that “The web is big. Really big. You just won’t believe how vastly, hugely mind-bogglingly big it is.”

But there is a problem in the second generation. As we know, this document web is for humans. And if we need to find something we simply search for it. As an example, if I search for the word “Mouse”. What do you think that I have searched for? Is it the animal mouse? Or is it the peripheral device mouse? The problem is the information retrieval Dilemma. There is an ambiguity when processing the natural language. Ambiguity means that for the same word we can get different meanings and concepts. In the second generation web, there is no explicit Semantic Web and Ontological Modelling.

Third Generation of  Semantic Web and Ontological Modelling

The third generation of Semantic Web and Ontological Modelling is the Web of Data. This is an upgrade of the web of documents. It includes a huge decentralized database. Which is also known and the knowledge base of data which the machines can access easily.

When migrating from web to web of data, there is a precondition. Which the content is interpreting  in a way that is understandable by machines. So when processing natural language, for information retrieval traditional technologies, are used. also, statistical models and machine learning are also used.

But in Semantic Web and Ontological Modelling, the natural language content should use annotations with meta data. And these meta data should be more understandable for the machines easily.

Web Content Understanding

When it comes to understanding the web content, as I discussed earlier, there is an ambiguity. As the example mentioned previously. Let us take the word “mouse”.The mouse can be an animal, a computer device. If we search for the entity mouse which is the animal. the class of the entity is the animal. And the animal is a subclass of living beings. If we search for the entity mouse which is the device. The class of the entity if a computer device. Which the computer device is a subclass of the peripheral device.

As a result, of this ambiguity problem in Semantic Web and Ontological Modelling, we should define the entities and the classes explicitly. Which means we should say the word mouse is actually referring to the word animal. So, the semantic meaning is expressing with the help of ontologies. which means the knowledge representation.

Semantic Web and Ontological Modelling Web of Data

To start with, Semantic Web is a global database which includes a universal network of propositions of semantics. These semantics make using structured and standardized ontologies or knowledge representations. By using the correct methodologies, the machines will be able to do the following,

• Can automatically process the meaning of information
• Heterogeneous data can be related and integrated
• Automatically deduct implicit information

 

Conclusion

to conclude , this article was a basic introduction of Semantic Web and Ontological Modelling. I explained the limitations of the web by categorizing it. The first generation internet. The second generation web. And the third generation Web of data. How important they are. And how to understand the content of the web by clearing the ambiguities. And the meaning of Semantic Web and the meaning of Ontology too.

This area is using mainly in agent systems, web intelligence to create the knowledge base. Furthermore, used in data mining and warehousing too. Most importantly to continue all these deep topics, we need to understand the basics if it. For that, I hope this article was a good foundation for future references.