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

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 Tim Berners-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??