A dramatic shift in business and technology is taking place as the Social Web (Web 2.0) evolves into the Semantic Web (Web 3.0) of the future. Networks link smartphones, in-car computers, televisions and home media networks to collectively provide instant and universal access to personal information and entertainment media. Integrated marketing campaigns feature an enticing mix of content and location-based and contextual-aware advertising delivered through digital signs and billboards. Highly targeted advertising is generated based digital profiles that describe the habits and preferences of an individual without revealing personal identifiable information, and then delivered through entertainment systems and mobile applications. Vast interconnected systems of distributed applications ingest data, generate feeds, and intelligently filter content based on usage patterns and preferences. This presentation, part one of three, covers the evolution of the Web, business models on the Web, and core elements of the semantic Web. Part two highlights existing products and systems that contain semantic Web elements. Part three covers 17 semantic Web application scenarios and forecasts the impact of Web 3.0 on marketing, advertising and business models.

1. Web 3.0 - Concepts, Technologies,
and Evolving Business Models
UCLA Extension | Course x861.409
Charles G. Hollins | cgh@ucla.edu

2. Introduction
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3. The Future of Advertising?
What aspects do you
think we'll see in the near
future?
Which do you think are
far off or unlikely to ever
materialize?
Are there any aspects of
these scenes similar to
applications that you
have used?
If these methods of advertising existed today do
you think they would be effective? Why or why
not?
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4. The Future of Advertising?
What are the similarities
and differences between
the real NEC system and
the one depicted in
Minority Report?
In an average week how
many public digital
screens are within your
view?
What are the benefits and constraints
of this type of advertising?
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5. Outline
 Introduction
 Evolution of Web
 How has the Web changed?
 What features have stayed the same?
 Semantic Web Concepts
 What technologies are being used to make the Web
smarter?
 Revenue Models
 How will the semantic Web impact revenue models?
 The Future of the Web
 What applications are on the horizon?
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6. About the Course
Sections Format
 Part 1 - Concepts  Terms and Concepts
(this deck)  Online Videos
 Part 2 - Existing Products  Group Discussion
and Applications Questions
 Part 3 - Future Semantic  Application Scenarios
Web Applications  Q&A
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7. Evolution of the Web
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8. See the Web Evolve in 4 Minutes
What terms were
you already
familiar with?
What terms have you
heard of any would like to
know more about?
How are users helping computers
to be more intelligent?
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9. The Pre-Web Internet
 Global Network
 Email and
Messaging
 File Sharing
 Remote Applications
 Document
Publishing
 Resource Sharing
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10. The Static Web
 Publication Medium
 Platform Independence
 Explosion of
Hyperlinked Content
and Media
 Search Engines and
Indexes
 Graphical User
Interface
 Internet Software for the
Masses
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11. The Dynamic Web
 Application Platform
 Web-Based
Applications
 Internet Services
 Data-Driven
Applications
 Interactive Web
Pages
 Location Independent
Computing
 Streaming Media
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12. The Social Web
 Extensible,
Interoperable
Applications
 Customization and
Personalization
 User Generated Content
Tools
 Tagged and Syndicated
Content
 Commenting, Feedback,
Reviews
 Data Feeds and Tools
 Community and
Collaboration
12

13. The Mobile Web
 Mobile Computing
Platforms
 Robust Mobile
Devices
 Access to the Web
from Anywhere
 Digital Entertainment
 Device Feature
Integration (camera,
GPS)
 Augmented Reality
 Encoded Data
13
Capture

14. Evolution of Business Models
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15. Brokerage
 Connect buyers and sellers
 Simple and focused function
 Transaction commission Demand Collection System
 Combinations
 B2B, B2C, C2C
 Payment: Buyer/Seller/Hybrid Virtual Marketplace
 Flat-Rate/Percentage
 Features: auction, etc.
 Web-Suited and Scalable Transaction Broker
Source: http://digitalenterprise.org/models/models.html Auction Broker
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16. Advertising
 Based on traditional media
 Broadcaster and advertiser
 Simple and focused function
 Models
 CPM - impression
 CPC - click
 CPA – action (register, install)
 Web-Suited and Scalable
 Content-driven Page Ad Units
 Profile and context targeting
 Metrics and analytics
Paid Placement Highly Targeted Ads
Source: http://digitalenterprise.org/models/models.html
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17. Sponsorship
 Similar to advertising
 Integrating brands into content
 Featured placements
 Page takeovers and UI skins
 Branded logotrademarks
Featured Placements
 Video strips and bugs
 Web-Suited and great for content
driven sites
Page Takeovers
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18. Infomediary
 Broker of consumer data
 Collect and aggregate usage data
 Common uses:
 Enhance marketing and advertising
campaigns Ad Networks
 Target ads based on behavior
 Web-Suited and Scalable
Market Research
Source: http://digitalenterprise.org/models/models.html
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19. Merchant
 More overhead and bigger risk
 Supply chain management
 Inventory investment
 Online sales and return processing
Virtual Merchant
 Multifaceted
 Buying
 Storage
 Selling
 Shipping Bit Vendor
 Variations
 Digital-only products
 Extended storefronts
Catalog Merchant
Source: http://digitalenterprise.org/models/models.html
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20. Manufacturer
 Direct sales from manufacturer to
consumer
 Similar to merchant but seller has
more control over inventory
 Inventory can be digital
 Example: the $10,000 email Direct Sales
 Many companies use EBay and
Amazon to implement this model
Source: http://digitalenterprise.org/models/models.html
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21. Affiliate
 Third-party that provides a
purchase lead (CPA) or add click-
through (CPC)
 Purchase point click-through from Purchase Affiliate
one site to the merchant site
 Merchant provides affiliate with a
percentage of transaction
 Very well-suited to the Web Conversion Affiliate
Ad Words Affiliate
Source: http://digitalenterprise.org/models/models.html
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22. Open Source
 Collaborative product development
 Freely available source code and
Open Source Platform
tools
 Results in robust platforms
 Revenue generated from
 Donations
 Support
 Documentation Open Content
 Integration
 Commercial versions
 Plug-ins and extensions
Open Source Software
Source: http://digitalenterprise.org/models/models.html
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23. Subscription
 Periodic fee for a service
 Free with limited scale
Free Trial then Subscription
 Free with limited features
 Free for trial period
 Free samples
Subscription or Limited Scale
 Well-suited for:
 Cloud services and applications
 Music and Video
 Consumer value often depends
on usage patterns Free Samples then
Subscription
 Predictable revenue stream
 (subscribers * rate)
Subscription or Limited Features
Source: http://digitalenterprise.org/models/models.html
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24. On-Demand
 Usage metered pay-as-you-go
 Popular for movie rentals
 Microtransactions & virtual
currency
 Popular in gaming Video on Demand
 Consumer must have a strong (VOD)
interest in the content to pay
Microtransactions and
Source: http://digitalenterprise.org/models/models.html Virtual Currency
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25. Business Models: Quiz
What business models do the following sites employ?
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26. Semantic Web Concepts
26

27. Collective Data and Intelligence
Elements Outcome
 User Generated  Massive Storehouse of
Content Information
 Content and Data  Multiple Viewpoints of a
Aggregation Subject or Object
 Framework for
 Blog Posts and Understanding and
Comments Evaluating Content
 Wikis  Object Meta Data
 Data feeds  Examples: Social
Bookmarking,
Photosynth
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28. Merging UGC into a Single View
Photosynth combines
related images into a
single view which can be
navigated.
The same technology was
originally used to combine
thousands of images of Venice
from various people into a
cohesive presentation.
What other types of content would this type of
aggregation and synthesis be well suited for?
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29. Entity Definition and Processing
Elements Outcome
 Massive Noun  Universal Catalog
Database  Context
 Reference consolidation  Disambiguation
 Item Roles  Links and Associations
 Item Attributes  Examples: Metaweb,
 Modeling of Freebase
Relationships
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30. A Database of Everything
What is Metaweb and
what problems does it aim
to solve? Who maintains
it?
How does Metaweb
make Web applications
more powerful?
How does Metaweb enable applications
and systems to better integrate?
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31. XML and Derived Languages
Elements Uses
 Text-Based  Defining Languages
 Meta Markup Language  Exchanging Data
 Elements and Attributes  Importing & Exporting
 Syntax and Parsing  Data Transformation
 Structure and Validation  Data in a Web Page
 example database record  Configuration Settings
 Parsers
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32. Descriptive Meta Data
Elements Outcome
 Geotagging  Associating an object
 Microformats with it's meaning
 Tag-Based  Self-defined structured
Folksonomies content
 Autotagging Systems  Objects that work well
with other applications
and tools
 Examples: Flickr Image
Mapping, ALIPR
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33. Microformats Add Meaning to Content
What are microformats?
How do the help make
data more portable and
meaningful?
What types of common
microformats do you
think exist?
What previously discussed elements of
the Web are being depicted here?
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34. Explicit Data Collection
Methods Outcome
 Items rated on a sliding  Digital DNA of Interests
scale and Preferences
 Ranked collection of
items
 Item marked as the best
of two or more options
 Wish list created
34

35. Implicit Data Collection
Methods Outcome
 Items viewed, added to a  Digital DNA of Interests
cart or purchased and Preferences
 Time spend on page or
item
 Items listened to or
watched
 Analyzing social network
likes and dislikes
 Analyzing feedback
sentiment
35

36. A Peek Under the Hood of Netflix
What was required in
order to win the
Netflix Prize?
Why was Napoleon
Dynamite an exception al
movie for the
collaborative filtering
algorithm?
What data does Netflix collect from it’s
users order to improve it’s
recommendations?
36

37. Information Filtering
Types Outcomes
 Profile-based  Recommendation
 Content-based engines
 Location-based  Targeting advertising
 Usage-based  Examples:
 Collaborative  Google Ad Sense
 Facebook Ads
 Netflix
Recommendations
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38. Recommendations Help Filter Content
How does Digg
determine what
articles a user would
like enjoy reading?
What applications have
you used that have
similar recommendation
features?
How do you think recommendations affect
user engagement?
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39. Cloud Computing
Types Outcome
 Online Data Storage  Scalable, location
 Online Web Applications independent computing
 Higher level of Web
 Online Commercial service provision
Applications
 Focus on function not
system
 Examples
 Dropbox
 Goggle Docs
 Twilio + Google Voice
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40. Dropbox Cloud File Storage
What common
problem does
Dropbox solve, and
how?
How does Dropbox
simplify the process of
backing up information
and sharing files?
What other types of Cloud services have
you heard of or used?
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41. Extensible Software
Types Outcome
 Browser Extensions  Ability to extend without
 Server Extensions rebuilding
 Social Network Platform  Interconnectivity
Apps between systems, Web
 Office Applications applications and
devices
 Widgets and Gadgets
 Examples
 Open Source Platforms
 WordPress and Joomla
 FireFox and Chrome
 LinkedIn Apps
41

42. LinkedIn Application Platform
What is a LinkedIn
application and why
might a user want to
install one on their
profile?
How are these
applications a win-win
deal for both the social
network and the
application developer?
An application used to refer to a program
installed on a computer. Now there are many
different types of applications. How many can
you identify?
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43. Smart Devices
Characteristics Outcome
 Radio-Frequency  Ubiquitous Computing
identification  Context Awareness
 Network Support  State Awareness
 Common, Extensible  Cooperative Processing
Operating Systems
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44. An Internet of Things
What is a system of
systems? What is The
Internet of Things?
What does the DIKW
Triangle represent? How
does this relate to the
"Collective Data and
Intelligence slide covered
earlier?
What scenarios involving the Internet of
Things might make your life easier?
44

45. Examples
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46. Freebase Paralax
How do entities
provide a framework
for working with data
in more powerful
ways?
What Web services were
used to visualize data
generated from Freebase?
This video showed a human using an Web
interface to query and present data. What
possibilities emerge when systems are
integrated into freebase?
46

47. Evri and Daylife
What are the main
features of Evri and
Daylife?
How do these services
relate to the semantic
Web?
What types of companies would
benefit from services offered by
DayLife?
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48. The Future of the Semantic Web
What aspects of the
semantic Web were
mentioned in this video?
Why are things such as
meta data, extensible
applications, smart devices,
information filtering, and
cloud computing important
aspects of the semantic
Web? How would you describe the difference
between the social Web and the semantic
Web?
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49. Q&A
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50. Thanks
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