Web 3.0 or The
Semantic Web
By: Konrad Sit
CCT355
November 21st 2011
Web 1.0
• Mostly flat information
• Some databases but content very
functional
• Little engagement or
interactivity
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.
Web 2.0
• Greater interactivity
• Growth of social media
/social networking
• Online communities
• created / social capital
Web 2.0
• Web 2.0
Web 3.0
• Joining up of information
• Data portability
• Browsers and search
engines become more
‘intelligent’
Differences
• Web 1.0 works but is clunky, not very
efficient, technically limited
Differences
• Web 2.0 is smoother, looks better, but
still lacks cohesion possibilities
• Web 3.0 has a greater scope of
exploration, limitless potential and is
smart
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
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
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
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
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.
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
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
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.
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“.
The End
Any Questions??