Research in ontological engineering includes the question of how non-expert users can be involved in the creation of semantically annotated ontologies and content[45] and how explicit knowledge can be extracted from user interaction within companies. Web 3.0 is probably the most nebulous and controversial term used to refer to the Semantic Web. After the explosive popularity and success of Web 2.0 as a term that represents the evolution of the Web towards social networks, crowdsourcing and user-generated content, many people have tried to conquer the market on the importance of the obvious next step, Web 3.0. The use of Web 3.0 as a nickname for the Semantic Web is probably the best known of these attempts. Semantics is a branch of linguistics that describes the meaning of characters and strings. The Semantic Web adds semantic information to web content and gives machines the ability to distinguish meanings (depending on the context, a character, for example a word, can have multiple meanings and different characters can have the same meaning). To this end, various standards and ontologies (amounts of information) are used to formulate machine-readable semantic metadata. The term “Semantic Web” is often used more specifically to refer to the formats and technologies that make it possible. [5] The collection, structuring and retrieval of linked data is made possible by technologies that provide a formal description of concepts, terms and relationships within a particular area of knowledge. These technologies are specified as W3C standards and include: Cory Doctorow`s (“Metacrap”) review is made 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 with 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.
[38] [39] Entities and ontologies are among the essential components of the Semantic Web. “Entity” is a term derived from semantics – it consists of an identifier and associated attributes. As an example, “Barack Obama” would be the identifier in an entity, while information such as “US President”, “Lawyer”, “Democrat” are the attributes, i.e. the descriptive properties. Entities, in turn, can be related to each other and thematically related or different. Even search engine-optimized websites are not only about good keywords, but also about semantic information that structures content and ensures a machine-readable information architecture. Be sure to embed structured data into websites and make web content as meaningful as possible using semantic standards. This way, you can improve your search engine rankings and be found by the audiences you want to target. To enable the encoding of semantics with data, technologies such as Resource Description Framework (RDF)[2] and Web Ontology Language (OWL)[3] 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 discussion and the use of heterogeneous data sources.
[4] Encoding similar information in a semantic web page may look like this: In the humanities, the term “semantics” refers to the meaning, such as.B. the meaning of a word. However, in the context of the Semantic Web, the term refers to formally defined meanings that can be used in calculations. In this sense, formal languages such as programming languages have a semantic component that determines the meaning of symbols and terms. For example, “x += y” has a defined meaning in programming languages such as C and Perl. 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 reasoning. 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 “type 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 for the field. […] Once one has learned a formal language of representation, it is still often much more laborious to express ideas in this 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 written structures. For example, if users search for the phrase “When did Barack Obama`s presidency begin?”, search engines would not simply return “January 20, 2009,” but the most appropriate hits for Barack Obama. In the Semantic Web, machines understand not only the content, but also the meaning of a search query and provide an accurate answer. In addition, the analysis of meanings in the Semantic Web includes not only text, but also images, sounds, numbers and symbols – that is, all the characteristics that have meaning. “Web Ontology Language.” (Yes, it should be “WOL.”) The Semantic Web standard used to define ontologies (sets of metadata) so that they can be used and understood in this environment. The availability of such standards has encouraged the development of an ecosystem of different tools from different providers, e.B.: database engines such as GraphDB that process RDF data (known as triplestores), ontology editors, markup tools that use text analysis to automatically generate semantic metadata, semantic search engines, and much more. 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. Other approaches to unified Semantic Web standards include contextual navigation language (CBL), which describes the relationships between information, and Web Ontology Language (OWL), which organizes and classifies information hierarchically. In addition, the following tags and standards help to create meta-statements, norms, and semantic rules: In order to achieve the Semantic Web, computer programs must learn to extract meaning. This is only possible if the existing or new www content contains structured data formulated in a machine-readable manner. Structured data is formulated using specific standards and classifications and encoded on websites in the form of schema markup and in-page markup.
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