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Web 3.0 or The Semantic Web

Web 3.0 or The Semantic Web

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Web 3.0 or The Semantic Web

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  1. Web 3.0 or The Semantic Web By: Konrad Sit CCT355 November 21st 2011

  2. Web 1.0 • Mostly flat information • Some databases but 
content very functional • Little engagement or interactivity

  3. Web 1.0 • Web 1.0 design elements • Some typical design elements of a Web 1.0 site include: • 1. Static pages instead of dynamic user-generated content. • 2. The use of framesets. • 3. HTML forms sent via email. A user would fill in a form, and upon clicking submit their email client would attempt to send an email containing the form's details.

  4. Web 2.0 • Greater interactivity • Growth of social media /social networking • Online communities • created / social capital

  5. Web 2.0 • Web 2.0

  6. Web 3.0 • Joining up of information • Data portability • Browsers and search engines become more ‘intelligent’

  7. Differences • Web 1.0 works but is clunky, not very efficient, technically limited

  8. Differences • Web 2.0 is smoother, looks better, but still lacks cohesion possibilities

  9. Web 3.0 has a greater scope of exploration, limitless potential and is smart

  10. So how do they match up • Web 3.0 is the integration of data on the internet • (Web 1.0) - Data is online + Super Apps • (Web 2.0) - Sites share via API’s and social networks • (Web 3.0) – Plugs into this massive amount of data we have made available on the web • We need to view the internet as a platform

  11. Barriers to web 3.0 • Building massively scalable data centers that are secure, reliable, and highly available is very complex and vary expensive. • Traditional client-server software development is still a painful and complex process • Deployment of applications is still difficult and the cost of maintenance is expensive

  12. Web 3.0 • Web 3.0 can think for itself • Connect big collections of databases on demand to allow for sorting of the vast amount of data on the internet

  13. Web 3.0 • Agreements are made on the structure of data and the way data is described • where the data is located is irrelevant • Linking data is the power of web 3.0 • Some believe that web 3.0 will be search engine advancement just as web 2.0 was social network advancement

  14. Web 3.0 as a platform • We will see data being integrated and applying it into innovative ways that were never possible before • Imagine The new shopping experience • Imagine The new travel experience • Major web sites will be transformed into web services • Major web sites will expose information to the world.

  15. Web 3.0 With Global Development • All you need to create an application is an idea, others can then add their talent • Every developer around the world can access the same powerful cloud infrastructures • Because code lives in the cloud, global talent pools can contribute to it • Because it runs in the cloud, a truly global market can subscribe to it as a service

  16. Web 3.0 the Semantic Web • The Semantic Web - coined by TimBerners-Lee, the man who invented the (first) World Wide Web • A place where machines can read Web pages much as we humans read them • A place where search engines and software agents can better troll the Net and find what we're looking for • Web as a universal medium for data, information, and knowledge exchange

  17. Some Challenges of Web 3.0 • Vastness: The World Wide Web contains at least 48 billion pages (as of August 2, 2009). The SNOMED CT medical terminology ontology contains 370,000 class names, and existing technology has not yet been able to eliminate all semantically duplicated terms. Any automated reasoning system will have to deal with truly huge inputs. • Vagueness: These are imprecise concepts like "young" or "tall". This arises from the vagueness of user queries, of concepts represented by content providers, of matching query terms to provider terms and of trying to combine different knowledge bases with overlapping but subtly different concepts. Fuzzy logic is the most common technique for dealing with vagueness.

  18. Continued… • Uncertainty: These are precise concepts with uncertain values. For example, a patient might present a set of symptoms which correspond to a number of different distinct diagnoses each with a different probability. Probabilistic reasoning techniques are generally employed to address uncertainty. • Inconsistency: These are logical contradictions which will inevitably arise during the development of large ontologies .Deductive reasoning fails catastrophically when faced with inconsistency, because "anything follows from a contradiction“.

  19. The End Any Questions??