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The Semantic Web

Semantic Web aims to create a meaning and define inter-relationship for information available on the web
In the early stages of the World Wide Web (web) it was necessary to develop standards to view web content (HTML language) and to create communication channels (N-Tier applications, email, ftp, etc.). As the web started to be the world’s largest knowledge base, accessible world wide, it became important to develop tools to transfer knowledge between cultures. However, it is still not possible for applications and agents to interoperate with other applications and agents without having a predefined, human created common framework of the meaning of the information being transferred on both sides. Semantic Web (SW) alleviates this problem by providing a common framework that allows data to be shared and reused across application, enterprise, and community boundaries [W3C Semantic Web, 2019].
A clear example on SW application is schema.org. Google, Bing, Yahoo use schema.org as a common medium to index embedded data.

In his vision of the SW, Tim Berners-Lee in [Berners-Lee & Fischetti 1999] says:

“I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web — the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize.”


The SW attempts to create a universal platform for data exchange. The SW aims to transform web content expressed in natural language, into a form that can be understood, interpreted and used by software agents, thus permitting them to find, share, integrate and extract information more easily. As SW is an evolving technology, some elements of the SW are expressed as prospective future possibilities that have yet to be implemented or realized, see [Shadbolt et al., 2006] for more details. Other elements were implemented and expressed in formal specifications and became known as ontology languages. An ontology typically consists of a hierarchical description of important concepts in a domain, along with descriptions of the properties of (the instances of) each concept [Horrocks & Patel-Schneider, 2003].

The SW is not different from the World Wide Web (Web). Most of it happens at the back-end where all analytics are taking place. SW enhances the Web by adding more utilities. People, who work in certain fields like research or domains like medicine, music etc., agree on common schemes (ontologies) for representing information they care about. As the Web allows more groups from different cultures and countries to develop these taxonomies, SW tools allow them to map their ontologies and translate their terms; gradually expanding the number of people and communities whose Web software can understand one another automatically [Lee et al., 2007].
[Shadbolt et al., 2006] in their vision towards a Semantic Web they believe that people and organizations should be obliged to make their data available, this can be driven by collaborative tools and communities of practices, also businesses could make product details available, etc. Those collected information can be then managed and linked into ontologies and integrated and reused by different applications.

The first standard ontology language that defined logical and semantic relations is Resource Description Framework (RDF) and RDF Schema (RDFS) which is the notation for RDF [W3C Semantic Web, 2005]. The current evolution of the SW ontology languages is the Web Ontology Language (OWL) and Graph Databases. Ontology languages intend to formally describe concepts, terms, and relationships within a given problem domain [W3C Semantic Web, 2019].

A visible example but limited in scope is the social networks websites like facebook and twitter where sets of custom tags available for people to use. In these schemes, people select common terms (tags) to describe the content they publish on the Web. This allows Web software to understand the tagged information. But in Social Media Semantic Web is more optimized, let us see how?

Social Semantic Web

Social Semantic Web creates an explicit Semantic representations on social interactions over the web. For instance [Bertola & Patti 2016] developed a framework where methods and tools from a set of disciplines, ranging from Semantic and Social Web to Natural Language Processing, provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions.
The Web of Things Use the Semantic Web (JSON-LD, RDFa, etc.) to discover and find Web Things Share Things via Social Networks to create the Social Web of Things Build a web-based smart home [Guinard & Trifa 2016].
Friend of a Friend (FOAF) is an ontology that describes persons, their activities and relations to other people and objects. FOAF integrates three kinds of network: social networks of human collaboration, friendship and association; representational networks that describe a simplified view of a cartoon universe in factual terms, and information networks that use Web-based linking to share independently published descriptions of this inter-connected world.

Conclusions

Semantic Web idea was to create a method of understanding for the web where machines can understand the information and its relations. The current evolution in Machine Learning, NLP and Social Semantic Web created a better Semantic Web. People privacy and personal information remains a challenge, tracking personal information and directing personalized search results and advertisements jeopardized the bright aim behind the Semantic Web.

References:

[Abusalah, 2008] Abusalah M., (2008). “Cross Language Information Retrieval Using Ontologies”, PhD Thesis, University of Sunderland.
[Berners-Lee & Fischetti 1999] Berners-Lee T., Fischetti M. (1999). “Weaving the Web”, Harper, San Francisco, chapter 12. ISBN 9780062515872.
[Bertola & Patti 2016] Bertola, Federico, and Viviana Patti. “Ontology-based affective models to organize artworks in the social semantic web.” Information Processing & Management 52.1 (2016): 139–162.
[Guinard & Trifa 2016] Guinard, Dominique, and Vlad Trifa. Building the web of things: with examples in node. js and raspberry pi. Manning Publications Co., 2016.
[Horrocks & Patel-Schneider, 2003] Horrocks I. and Patel-Schneider P. F. (2003). “Three theses of representation in the semantic web”. In: Proc. of the Twelfth International World Wide Web Conference (WWW 2003), pages 39–47. ACM, 2003.
[Lee et al., 2007] Lee F., Herman I., Hongsermeier T., Neumann E., and Stephens S. (2007). “The Semantic Web in Action.” Scientific American, vol. 297, pp. 90–97.
[Shadbolt et al., 2006] Shadbolt N., Berners-Lee T. and Hall W. (2006). “The Semantic Web Revisited”. IEEE Intelligent Systems 21(3) pp. 96–101.
[W3C Semantic Web, 2019] “W3C Semantic Web”, website: http://www.w3.org/2001/sw/ Last visited 13–09–2019.

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