Semantic Web: Difference between revisions

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The goal of the Semantic Web is to associate meaning with the data on the Web and to exploit the wealth of data on the Web through more intelligent (meaningful) processing. A semantic approach to data processing, such as the use of ontologies or knowledge bases, has increasingly been integrated with other AI techniques, especially machine learning (ML) and natural language processing (NLP).<ref>https://www.quora.com/What-is-the-main-goal-of-semantic-web</ref>
The goal of the Semantic Web is to associate meaning with the data on the Web and to exploit the wealth of data on the Web through more intelligent (meaningful) processing. A semantic approach to data processing, such as the use of ontologies or knowledge bases, has increasingly been integrated with other AI techniques, especially machine learning (ML) and natural language processing (NLP).<ref>https://www.quora.com/What-is-the-main-goal-of-semantic-web</ref>
* Examples of non-semantic elements: <div> and <span> - Tells nothing about its content.<ref>https://www.w3schools.com/html/html5_semantic_elements.asp</ref>
* Examples of semantic elements: <form>, <table>, and <article> - Clearly defines its content.<ref>https://www.w3schools.com/html/html5_semantic_elements.asp</ref>





Revision as of 18:39, 22 January 2018

Web Science[1]

Text web vs multimedia web[edit]

Text Web Multimedia web
Text is presented with minimal markup Text is heavily styled often with CSS
Pages are generally static (dynamic elements are certainly feasible, but they are often minimally styled) Pages are generally dynamic
Page is designed to be read Page is designed to be seen, heard, and read
Design of content and markup is often rather sparse and utilitarian Design of content and markup is often called "rich media".

The aims of the semantic web[edit]

The goal of the Semantic Web is to associate meaning with the data on the Web and to exploit the wealth of data on the Web through more intelligent (meaningful) processing. A semantic approach to data processing, such as the use of ontologies or knowledge bases, has increasingly been integrated with other AI techniques, especially machine learning (ML) and natural language processing (NLP).[2]