Although learning the basics of HTML is relatively easy, learning a language or knowledge representation tool requires the author to learn the methods of abstraction of representation and their impact on thought. For example, understanding the class-instance relationship or the superclass-subclass relationship is not limited to understanding that one concept is a “type” of another concept. […] These abstractions are taught to computer scientists in general and knowledge engineers in particular, but do not correspond to the similar meaning of natural language of being a “kind of” something. The effective use of such formal representation requires that the author become an experienced knowledge engineer in addition to all the other skills required by the field. […] Once you have learned a formal language of representation, it is often even more tedious to express ideas in that representation than in a less formal representation […]. In fact, it is a form of programming based on the declaration of semantic data that requires an understanding of how argumentation algorithms interpret the structures created. The concept of the semantic network model was developed in the early 1960s by researchers such as cognitive scientist Allan M. Collins, linguist M. Ross Quillian, and psychologist Elizabeth F.
Loftus as a form of semantic representation of structured knowledge. When applied in the context of the modern Internet, it expands the network of linked and human-readable websites by inserting machine-readable metadata on pages and their relationship to each other. This allows automated agents to access the web more intelligently and perform more tasks on behalf of users. The term “Semantic Web” was coined by Tim Berners-Lee, the inventor of the World Wide Web and director of the World Wide Web Consortium (“W3C”), which oversees the development of proposed standards for the Semantic Web. He defines the Semantic Web as “a network of data that can be processed directly and indirectly by machines.” An exact definition of Web 3.0 is difficult to determine, but most descriptions agree that a fundamental feature of it is the ability to make connections and derive meaning from them – essentially, the Web is becoming “smarter.” This has led to expressions such as the Semantic Web or the Intelligent Web in relation to Web 3.0. The first research group to explicitly focus on the corporate Semantic Web was the ACACIA team of INRIA-Sophia-Antipolis, founded in 2002. The results of their work include the RDF(S) corese based search engine and the application of Semantic Web technology in the field of distributed artificial intelligence for knowledge management (e.B. ontologies and multi-agent systems for the semantic web of enterprises)  and e-learning.  People keep wondering what Web 3.0 is. I think if you have an overlay of scalable vector graphics – everything splashes and bends and looks foggy – on Web 2.0 and access to a semantic web built into a huge data room, you could have access to an incredible data resource. Web 2.0 refers to websites and applications that use user-generated content for end users.
Web 2.0 is used on many websites today and focuses primarily on user interactivity and collaboration. Web 2.0 has also focused on providing more universal network connectivity and communication channels. The difference between Web 2.0 and 3.0 is that Web 3.0 focuses more on using technologies such as machine learning and AI to deliver relevant content to each user, rather than content provided by other end users. Web 2.0 essentially allows users to collaborate on website content and sometimes collaborate, while Web 3.0 will most likely pass these tasks on to the Semantic Web and AI technologies. Enthusiasm for the Semantic Web could be tempered by concerns about censorship and privacy. For example, text analysis techniques can now be easily circumvented by using other words, such as metaphors, or by using images instead of words. An advanced implementation of the Semantic Web would make it much easier for governments to control the viewing and creation of information online, as this information would be much easier to understand for an automated content blocking machine. In addition, the issue was also raised that when using FOAF files and geolocation metadata, very little anonymity would be associated with authoring articles about things like a personal blog. Some of these concerns have been addressed in the Policy Aware Web project and are an active topic of research and development. The idea behind the Semantic Web is to categorize and store information in a way that teaches a system what certain data means.
In other words, a website should be able to understand words in search queries in the same way as a human so that it can generate and share better content. This system will also use AI; The Semantic Web will teach a computer what the data means, and then the AI will take the information and use it. As if the confusion between these terms wasn`t enough, it`s not even close to the end of the story. People were working with semantic technologies long before the first Semantic Web technology was even conceived. So what is the relationship between semantic technologies and the Semantic Web? More information about this in the lessons to come. Research in ontological engineering includes the question of how non-expert users can be involved in creating semantically annotated ontologies and content and extract explicit knowledge from user interaction in companies. from the document to schema.org/Person (green border in the figure) allow to derive the next triple, given the owl semantics (red dotted line in the second figure): According to Marshall and Shipman, the tacit and changing nature of a lot of knowledge contributes to the problem of knowledge engineering and limits the applicability of the Semantic Web to certain domains. Another subject they highlight is that of expressions specific to a field or organisation which must be resolved by means of Community agreements and not only by technical means.  It turns out that communities and organizations specialized for internal projects have tended to adopt Semantic Web technologies that are larger than peripheral and less specialized communities.  Practical deployment constraints have proven less difficult when the scope and scope are more limited than those of the general public and the World Wide Web.
 Cory Doctorow`s (“Metacrap”) criticism comes from the perspective of human behavior and personal preferences. For example, users may insert incorrect metadata into web pages to mislead semantic web engines that naively assume metadata accuracy. This phenomenon was known in metatags, which led the Altavista ranking algorithm to increase the ranking of some websites: the Google indexing engine specifically looks for such manipulation attempts. Peter Gärdenfors and Timo Honkela point out that logic-based Semantic Web technologies cover only a fraction of the relevant semantic phenomena.   “Semantic Web” is sometimes used as a synonym for “Web 3.0″, although the definition of each term varies. To enable the encoding of semantics with data, technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are used. These technologies are used to formally represent metadata. For example, ontology can describe concepts, relationships between entities, and categories of things. This integrated semantics offers significant benefits such as data thinking and the use of heterogeneous data sources.
 Web 3.0 can be built with artificial intelligence (AI), the Semantic Web, and ubiquitous properties in mind. .