Transcript PowerPoint Template

Making Sense of the Semantic Web
Nova Spivack, CEO and Founder, Radar Networks
May 19, 2008
About This Talk
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Making sense of the semantic sector
•
How the Semantic Web works
•
Future outlook
•
Twine.com
•
Q&A
The Big Opportunity…
The social graph just connects people
The semantic graph connects everything
People
Companies
Emails
Places
And it uses richer
semantics to enable:
Products
Interests
Services
Web Pages
Activities
•Better search
•More targeted ads
•Smarter collaboration
•Deeper integration
Documents
Projects
•Richer content
Events
Multimedia
Groups
•Better personalization
The Third Decade of the Web
The next generation: A period in time, not a technology…
•
Enrich the structure of the Web with Semantics
Improve the quality of search, collaboration, publishing, advertising
• Enables applications to become more integrated and intelligent
•
•
Transform the Web from fileserver to database
Semantic technologies will play a key role
• There will be more meaning to relationships between businesses,
colleagues and friends based on interests and behavior online
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The Intelligence is in the Connections
Intelligent Web
Web 4.0
Connections between Information
Web OS
2020 - 2030
Intelligent personal agents
Semantic Web
Web 3.0
Distributed Search
SWRL
OWL
2010 - 2020
SPARQL
Semantic Databases
OpenID AJAX
Semantic Search
ATOM
Widgets
Social Web
RSS
Mashups
P2P RDF
Office 2.0
Javascript
Flash
SOAP XML
2000 - 2010 Weblogs Social Media Sharing
Java
The Web
HTML
SaaS Social Networking
HTTP
Directory Portals Wikis
VR
Keyword Search Lightweight Collaboration
The PC
BBS Gopher
Websites
1990 - 2000
SQL
MMO’s MacOS
Groupware
SGML
Databases
Windows
File Servers
Web 2.0
Web 1.0
The Internet
FTP
IRC Email
PC Era
1980 - 1990
USENET
PC’s
File Systems
Connections between people
Search of the Future
The Intelligent Web
Web 4.0
Productivity of Search
2020 - 2030
The Semantic Web
Web 3.0
2010 - 2020
Web
1.0
1990 - 2000
Web
2.0
2000 - 2010
Natural language search
Human tagging
Keyword search
The Desktop
Directories
PC Era
Semantic Search
Machine SEMANTIC auto-tagging
The Social Web
The World Wide Web
Reasoning
1980 - 1990
Files & Folders
Databases
Directories
Amount of data
Five Approaches to Semantics
Tagging
Statistics
Semantic
Web
Linguistics
Artificial
Intelligence
The Tagging Approach
Tagging
Technorati
Del.icio.us
Pros
• Easy for users to add and read tags
• Tags are just strings
• No algorithms or ontologies to deal with
• No technology to learn
Cons
• Easy for users to add and read tags
• Tags are just strings
• No algorithms or ontologies to deal with
• No technology to learn
Flickr
Wikipedia
The Statistical Approach
Statistics
Pros
Cons
Google
Lucene
Autonomy
• Pure mathematical algorithms
• Massively scaleable
• Language independent
• 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
The Linguistic Approach
Linguistics
Pros
Cons
Powerset
Hakia
Inxight,
Attensity,
and
others…
• True language understanding
• Extract knowledge from text
• Best for search for particular facts or relationships
• More precise queries
• Computationally intensive
• Difficult to scale
• Lots of errors
• Language-dependent
The Semantic Web Approach
Semantic
Web
Pros
Cons
Radar
Networks
DBpedia
Project
• More precise queries
• Smarter apps with less work
• Not as computationally intensive
• Share & link data between apps
• Works for both unstructured and structured data
• Lack of tools
• Difficult to scale
• Who makes all the metadata?
Metaweb
The Artificial Intelligence Approach
Artificial
Intelligence
Cycorp
Pros
• This is the holy grail!!!!
• Approximates the expertise and common sense reasoning
ability of a human domain expert
• Reasoning / inferencing, discovery, automated assistance,
learning and self-modification, question answering, etc.
Cons
• This is the holy grail!!!!
• Computationally intensive
• Hard to program and design
• Takes a long time and a lot of work to reach critical mass
of knowledge
Make the Data Smarter
The Approaches Compared
A.I.
Semantic
Web
Linguistics
Tagging
Statistics
Make the software smarter
Two Paths to Adding Semantics
“Top-Down”
(Contemporary)
“Bottom-Up”
(Classic)
• Add semantic metadata
to pages and databases
all over the Web
• Every Website becomes
semantic
• Everyone has to learn
RDF/OWL
• 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
In Practice: Hybrid Approach Works Best
Tagging
Semantic Web
Top-down
Statistics
Linguistics
Bottom-up
Artificial intelligence
A Higher Resolution Web
Joe
Person
Subscriber to
IBM.com
Web Site
Lives in
IBM
Company
Palo Alto
City
Fan of
Publisher of
Lives in
Employee of
Fan of
Dave.com
RSS Feed
Coldplay
Band
Sue
Person
Jane
Person
Friend of
Member of
Depiction of
Married to
Design
Team
Group
Source of
Member
of
Dave.com
Weblog
Bob
Person
Member of
Member of
Author of
Stanford
Alumnae
Group
Dave
Person
Member of
123.JPG
Photo
Depiction of
The Web Is the Database!
Application A
Application B
IBM.com
Web Site
Joe
Person
Lives in
IBM
Company
Palo Alto
City
Publisher of
Fan of
Subscriber to
Lives in
Employee of
Dave.com
RSS Feed
Coldplay
Band
Sue
Person
Jane
Person
Fan of
Friend of
Member of
Depiction of
Design
Team
Group
Source of
Married to
Member
of
Dave.com
Weblog
123.JPG
Photo
Bob
Person
Depiction of
Member of
Author of
Stanford
Alumnae
Group
Dave
Person
Member of
Member of
Smart Data
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Smart Data is data that carries whatever is needed to
make use of it:
Software can become dumber and more generic, yet
ultimately be smarter
The smarts moves into the data itself rather than being
hard-coded into the software
The Semantic Web is a Key Enabler
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Moves the “intelligence” out of applications, into
the data
Data becomes self-describing; Meaning of data becomes
part of the data
• Data = Metadata.
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Just-in-time data
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Applications can pull the schema for data only when the
data is actually needed, rather than having to anticipate
it
The Semantic Web = Open database layer for the Web
User
Profiles
Web
Content
Ads &
Listings
Data
Records
Open Query Interfaces
Open Data Mapping
Open Data Records
Open Rules
Open Ontologies
Apps &
Services
Semantic Web Technologies
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RDF – Store data as triples
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OWL – Define systems of concepts called ontologies
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Sparql – Query data in RDF
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SWRL – Define rules
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GRDDL – Transform data to RDF
RDF “Triples”
SUBJECT
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•
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PREDICATE
OBJECT
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
Source: http://www.w3.org/TR/rdf-concepts/#section-triples
Semantic Web Data is Self-Describing Linked Data
Ontologies
Definition
Definition
Definition
Definition
Data Record ID
Definition
Definition
Definition
Field 1
Value
Field 2
Value
Field 3
Value
Field 4
Value
RDBMS vs Triplestore
Person Table
ID
001
002
003
004
f_name
jim
nova
chris
lew
Colleagues Table
SRC-ID
001
001
001
001
002
002
002
002
003
003
003
003
004
004
004
004
TGT-ID
001
002
003
004
001
002
003
004
001
002
003
004
001
002
003
004
S P O
l_name
wissner
spivack
jones
tucker
Subject Predicate
Object
001
001
001
001
002
002
002
002
003
003
003
003
004
004
004
Person
Jim
Wissner
002
Person
Nova
Spivack
003
Person
Chris
Jones
004
Person
Lew
Tucker
isA
firstName
lastName
hasColleague
isA
firstName
lastName
hasColleague
isA
firstName
lastName
hasColleague
isA
firstName
lastName
Merging Databases in RDF is Easy
S P O
S P O
S P O
The Growing Linked Data Universe
The Growing Semantic Web
ONLINE SERVICES
CONSUMERS
DEVELOPERS
APPLICATIONS
Future Outlook
• Early-Adoption
• A few killer apps emerge
2007 – • Other apps start to integrate
2009
2010 –
2020
• Mainstream Adoption
• Semantics widely used in Web content and apps
• Next big cycle: Reasoning and A.I.
• The Intelligent Web
2020 + • The Web learns and thinks collectively
The Future of the Platform…
• The Desktop
is the
platform
1980’s
1990’s
• The Browser /
Server is the
platform
• Web Services
are the
platform
2000’s
2010’s
• The Semantic
Web is the
platform
• The Web OS
is the
platform
2020’s
2030’s
• The Human
Body is the
platform…?
A Mainstream Application
of the Semantic Web…
Twine Overview
Organize. Share. Discover.
Around your interests
Using the Semantic Web
What Can You Do With Twine?
Organize
Share
Discover
• Collect from the Web and e-mail
• Manage the items you collected
• Author & share content
• Discuss & collaborate
• Track Interests
• Search & explore
• Get recommendations
Differentiation
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Facebook - For your relationships
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LinkedIn - For your career
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Twine - For your interests
Twine is for sharing information
around interests
Twine is Smart
Semantic tagging
Semantic linking
Organize
Content
(all types)
Share
Discover
Recommendations
Semantic Search
Let’s take a look at Twine…
(demo of Twine site…)
Radar Networks’ Semantic Web Platform
Web App
Twine.com
REST API
SPARQL
User Portal
Bookmarklet
& Email
RSS Feeds
Cache
AJAX, Jetty, PicoContainer, Java, XML, SPARQL Jena, ATOM
KnowledgeBase
Semantic Object
Class inferencing
Object Query
& Cache
Tuple
Query
Platform
Cache
RDF, OWL
TupleStore service
SQL Query
Generator
Access Control
Predicate
Inferencing
Remote
Access
Cache
RDF, OWL, SQL Mina
SQL Database
WebDAV File Store
Storage
Relational database
Postgres,
Solaris
Flat File Store
webDAV, Isilon
cluster
Target Customer
Twine is for active users of the Web,
including consumers and professionals,
who create, find and share information
about their interests
Market Opportunities for Twine
Individuals
Groups and Teams
• Consumers
• Professionals
• Bloggers
• Thought leaders
• Professors/Teachers
• Work groups
• Project teams
• Customer support
• Support Groups
• User Groups
Communities
Corporations and
Associations
• Special events
• Interest networks
• Products
• Special interest groups
• Activity organization
• Knowledge sharing
• One to many communications
• Promotions/Advertising
Contact Info
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Visit www.twine.com to sign up for the invite beta waitlist
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You can email me at [email protected]
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My blog is at http://www.mindingtheplanet.net
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Thanks!
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 http://creativecommons.org/licenses/by/3.0/ 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
http://www.mindingtheplanet.net