1. Making Sense of the Semantic Web
Nova Spivack
CEO & Founder
Radar Networks

2. About This Talk Making sense of the semantic sector
Making the Semantic Web more useable
Future outlook
Twine.com
Q & A

3. The Big Opportunity… The social graph just connects people
Groups
The semantic graph connects everything… s
Companies
Products
Services
Web Pages
Multimedia
Documents
Events
Projects
Activities
Interests
Places
Better search
More targeted ads
Smarter collaboration
Deeper integration
Richer content
Better personalization 3

4. The third decade of the Web
A period in time, not a technology…
Enrich the structure of the Web
Improve the quality of search, collaboration, publishing, advertising
Enables applications to become more integrated and intelligent
Transform Web from fileserver to database
Semantic technologies will play a key role

5. The Intelligence is in the Connections
Intelligent Web
Web 4.0
Web OS
Intelligent personal agents
Semantic Web
Web 3.0
Distributed Search
SWRL
OWL
SPARQL
Semantic Databases
OpenID
AJAX
Connections between Information
Social Web
Semantic Search
ATOM
Widgets
RSS
P2P
RDF
Mashups
Web 2.0
Office 2.0
Javascript
Flash
SOAP
XML
Weblogs
Social Media Sharing
The Web
Java
HTML
SaaS
Social Networking
HTTP
Directory Portals
Wikis VR
Web 1.0
Keyword Search
Lightweight Collaboration
The PC
BBS
Websites
Gopher
MMO’s
MacOS
SQL
Groupware
SGML
Databases
Windows
The Internet
File Servers
PC Era
FTP
IRC
USENET
PC’s
File Systems
Connections between people

6. Beyond the Limits of Keyword Search
The Intelligent Web
Web 4.0
Productivity of Search
Reasoning
The Semantic Web
Web 3.0
Semantic Search
The Social Web
Natural language search
Web 2.0
The World Wide Web
Tagging
Web 1.0
Keyword search
The Desktop
PC Era
Directories
Files & Folders
Databases
Amount of data

7. A Higher Resolution Web
IBM.com
Web Site
Joe
Person
Lives in
Palo Alto
City
IBM
Company
Publisher of
Fan of
Subscriber to
Lives in
Employee of
Sue
Person
Jane
Person
Dave.com
RSS Feed
Coldplay
Band
Fan of
Friend of
Member of
Depiction of
Design
Team
Group
Married to
Source of
Member of
123.JPG
Photo
Dave.com
Weblog
Bob
Person
Depiction of
Member of
Dave
Person
Stanford
Alumnae
Group
Member of
Author of
Member of

8. Five Approaches to Semantics
Tagging
Statistics
Linguistics
Semantic Web
Artificial Intelligence

9. The Tagging Approach Pros Cons Technorati Del.icio.us Flickr Wikipedia
Easy for users to add and read tags
Tags are just strings
No algorithms or ontologies to deal with
No technology to learn
Cons
Technorati
Del.icio.us
Flickr
Wikipedia

10. The Statistical Approach
Pros:
Pure mathematical algorithms
Massively scaleable
Language independent
Cons:
No understanding of the content
Hard to craft good queries
Best for finding really popular things – not good at finding needles in haystacks
Not good for structured data
Google
Lucene
Autonomy

11. The Linguistic Approach
Pros:
True language understanding
Extract knowledge from text
Best for search for particular facts or relationships
More precise queries
Cons:
Computationally intensive
Difficult to scale
Lots of errors
Language-dependent
Powerset
Hakia
Inxight, Attensity, and others…

12. The Semantic Web Approach
Pros:
More precise queries
Smarter apps with less work
Not as computationally intensive
Share & link data between apps
Works for both unstructured and structured data
Cons:
Lack of tools
Difficult to scale
Who makes all the metadata?
Radar Networks
DBpedia Project
Metaweb

13. The Artificial Intelligence Approach
Pros:
Smart in narrow domains
Answer questions intelligently
Reasoning and learning
Cons:
Computationally intensive
Difficult to scale
Extremely hard to program
Does not work well outside of narrow domains
Training takes a lot of work
Cycorp

14. The Approaches Compared
Make the Data Smarter
A.I.
Semantic
Web
Linguistics
Tagging
Statistics
Make the software smarter

15. Two Paths to Adding Semantics
“Bottom-Up” (Classic)
Add semantic metadata to pages and databases all over the Web
Every Website becomes semantic
Everyone has to learn RDF/OWL
“Top-Down” (Contemporary)
Automatically generate semantic metadata for vertical domains
Create services that provide this as an overlay to non-semantic Web
Nobody has to learn RDF/OWL
-- Alex Iskold

16. In Practice: Hybrid Approach Works Best
Tagging
Semantic Web
Top-down
Statistics
Linguistics
Bottom-up
Artificial intelligence

17. The Semantic Web is a Key Enabler
Moves the “intelligence” out of applications, into the data
Data becomes self-describing; Meaning of data becomes part of the data
Apps can become smarter with less work, because the data carries knowledge about what it is and how to use it
Data can be shared and linked more easily

18. The Semantic Web = Open database layer for the Web
User
Profiles
Web
Content
Data
Records
Apps &
Services
Ads &
Listings
Open Data Mappings
Open Data Records
Open Rules
Open Ontologies
Open Query Interfaces 18

19. Semantic Web Open Standards
RDF – Store data as “triples”
OWL – Define systems of concepts called “ontologies”
Sparql – Query data in RDF
SWRL – Define rules
GRDDL – Transform data to RDF

20. Subject Object RDF “Triples” Predicate
the subject, which is an RDF URI reference or a blank node
the predicate, which is an RDF URI reference
the object, which is an RDF URI reference, a literal or a blank node
Subject
Object
Predicate
Source:

21. Semantic Web Data is Self-Describing Linked Data
Ontologies
Definition
Definition
Definition
Definition
Data Record ID
Field 1 Value
Field 2 Value
Field 3 Value
Field 4 Value
Definition
Definition
Definition

22. RDBMS vs Triplestore S P O Person Table ID 001 002 003 004 f_name jim
Subject Predicate Object
001 isA Person
001 firstName Jim
001 lastName Wissner
001 hasColleague 002
002 isA Person
002 firstName Nova
002 lastName Spivack
002 hasColleague 003
003 isA Person
003 firstName Chris
003 lastName Jones
003 hasColleague 004
004 isA Person
004 firstName Lew
004 lastName Tucker ID
001
002
003
004
f_name
jim
nova
chris
lew
l_name
wissner
spivack
jones
tucker
Colleagues Table
SRC-ID
001
002
003
004
TGT-ID
001
002
003
004

23. Merging Databases in RDF is EasyP O S P O S P O

24. The Web IS the Database! Application A Application B Coldplay Band
Palo Alto
City
Jane
Person
Company
IBM
Dave
Bob
Design
Team
Group
Stanford
Alumnae
Web Site
IBM.com
123.JPG
Photo
Dave.com
Weblog
Sue
Joe
RSS Feed
Lives in
Publisher of
Friend of
Depiction of
Member of
Married to
Member of
Fan of
Subscriber to
Source of
Author of
Employee of

25. Are RDF/OWL the Only Way to Express Semantics?
Other contenders:
String tags
Taxonomies and controlled vocabularies
Microformats
Ad hoc [name, value] pairs
Alternative semantic metadata notations

26. One Semantic Web or Many?
The answer is….Both
The Semantic Web is a web of semantic webs
Each of us may have our own semantic web…

27. Why has it Taken So Long?
The Dream of the Semantic Web has been slow to arrive
The original vision was too focused on A.I.
Technologies and tools were insufficient
Needs for open data on the Web were not strong enough
Keyword search and tagging were good enough…for a while
Lack of end-user facing killer apps
Lots of misunderstanding to clear up

28. Crossing the Chasm… Communicating the vision Technology progress
Focus on open data, not A.I.
Technology progress
Standards & tools finally maturing
Needs were not strong enough
Keyword search and tagging not as productive anymore
Apps need better way to share data
Killer apps and content
Several companies are starting to expose data to the Semantic Web. Soon there will be a lot of data.
Market Education
Show the market what the benefits are

29. Future Outlook 2007 – 2009 2010 – 2020 2020 + Early-Adoption
A few killer apps emerge
Other apps start to integrate
2010 – 2020
Mainstream Adoption
Semantics widely used in Web content and apps
2020 +
Next big cycle: Reasoning and A.I.
The Intelligent Web
The Web learns and thinks collectively

30. The Future of the Platform…
1980’s -- The desktop is the platform
1990’s -- The browser is the platform
2000’s -- The Web is the platform
2010’s -- The Graph is the platform
2020’s -- The network is the platform
2030’s -- The body is the platform…?

31. A Mainstream Application of the Semantic Web…31

32. What is Twine?
Twine is a new service for managing & sharing information on the Web
Works for content, knowledge, data, or any other kinds of information
Designed for individuals and groups that need a better way to organize, search, share and keep track of their information

33. How Twine Works
Collect or author structured or unstructured information into Twine via , the Web or the desktop
Twine creates a knowledge web automatically
Understands, tags & links information automatically
Automatically does further research for you on the Web
Organizes information automatically
Provides semantic search, discovery & interest tracking
Helps you connect with other people & groups to grow and share knowledge webs around common interests

34. Individuals Groups & Teams Use-Cases
Collect & author information about interests
Share with your friends & colleagues
Find and discover things more relevantly
Groups & Teams
Manage content & knowledge related to common interests, goals, or activities
Leverage and contribute to collective intelligence
Collaborate more productively

35. Contact Info
Visit to sign up for the invite beta wait-list
You can me at
My blog is at
Thanks!

36. Rights
This presentation is licensed under the Creative Commons Attribution License.
Details: This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.
If you reproduce or redistribute in whole or in part, please give attribution to Nova Spivack, with a link to