PowerPoint Template

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

And it uses richer semantics to enable Better
search More targeted ads Smarter
collaboration Deeper integration Richer
content Better personalization
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
A Higher Resolution Web

IBM.com Web Site
Joe Person
IBM Company
Lives in
Palo Alto City
Publisher of
Fan of
Lives in
Subscriber to
Employee of
Sue Person
Jane Person
Dave.com RSS Feed
Fan of
Coldplay Band
Friend of
Member of
Depiction of
Design Team Group
Married to
Source of
Member of
123.JPG Photo
Bob Person
Dave.com Weblog
Depiction of
Member of
Member of
Dave Person
Stanford Alumnae Group
Author of
Member of
6
The Web IS the Database!

Application A
Application B
7
The Intelligence is in the Connections

Intelligent Web
Web 4.0
Web OS
2020 - 2030
Intelligent personal agents
Web 3.0
Semantic Web
Distributed Search
SWRL
2010 - 2020
OWL
SPARQL
Semantic Databases
AJAX
OpenID
Connections between Information
Semantic Search
Social Web
Widgets
ATOM
RSS
RDF
Mashups
P2P
Office 2.0
Web 2.0
Javascript
Flash
SOAP
Weblogs
XML
Social Media Sharing
2000 - 2010
The Web
Java
HTML
Social Networking
SaaS
HTTP
Wikis
Directory Portals
VR
Keyword Search
Lightweight Collaboration
Web 1.0
The PC
Websites
BBS
Gopher
1990 - 2000
SQL
MacOS
MMOs
Groupware
Databases
SGML
Windows
File Servers
The Internet
PC Era
Email
IRC
1980 - 1990
FTP
USENET
PCs
File Systems
Connections between people
8
Beyond the Limits of Keyword Search

The Intelligent Web
Web 4.0
Productivity of Search
2020 - 2030
Reasoning
The Semantic Web
Web 3.0
Semantic Search
2010 - 2020
The Social Web
Natural language search
Web 2.0
The World Wide Web
2000 - 2010
Tagging
Web 1.0
1990 - 2000
Keyword search
The Desktop
Directories
PC Era
1980 - 1990
Files Folders
Databases
Amount of data
9
The Future of the Platform

• 1980s -- The Desktop is the platform
• 1990s -- The Browser / Server is the platform
• 2000s -- Web Services are the platform
• 2010s -- The Semantic Web is the platform
• 2020s -- The WebOS is the platform
• 2030s -- The Human Body is the platform?

10
Five Approaches to Semantics

• Tagging
• Statistics
• Linguistics
• Semantic Web
• Artificial Intelligence

11
The Tagging Approach

• 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

• Technorati
• Del.icio.us
• Flickr
• Wikipedia

12
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

13
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

14
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

15
The Artificial Intelligence Approach

• 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

• Cycorp

16
The Approaches Compared
Make the Data Smarter

A.I.
Semantic Web
Linguistics
Tagging
Statistics
Make the software smarter
17
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

18
In Practice Hybrid Approach Works Best

• Tagging
• Semantic Web
• Top-down
• Statistics
• Linguistics
• Bottom-up
• Artificial intelligence

19
Smart Data

• Smart Data is data that carries whatever is
  needed to make use of it
• Definition of intended meaning and schema
• Policies and permissions
• Context (links, etc.)
• History and schedule
• Authenticity
• Sentiment
• Behavior (each piece of data may someday have its
  own rules and/or agent(s) that seek to move the
  data to where it is needed, connect it, maintain
  it, protect it, improve it, etc.)
• Software can become dumber and more generice, yet
  ultimately be smarter the smarts moves into the
  data itself rather than being hard-coded into the
  software

20
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 Data Metadata.
• Data can be shared and linked more easily
• Just-in-time schemas applications can pull the
  schema for data only when the data is actually
  needed, rather than having to anticipate it

21
The Semantic Web Open database layer for the Web

22
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

23
RDF Triples

• 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
24
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
25
RDBMS vs Triplestore

Person Table
S
P
O
Subject Predicate Object 001 isA Person 001 first
Name Jim 001 lastName Wissner 001 hasColleague 0
02 002 isA Person 002 firstName Nova 002 lastNam
e Spivack 002 hasColleague 003 003 isA Person 00
3 firstName Chris 003 lastName Jones 003 hasColl
eague 004 004 isA Person 004 firstName Lew 004 l
astName Tucker
f_name jim nova chris lew
ID 001 002 003 004
l_name wissner spivack jones tucker
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
26
Merging Databases in RDF is Easy

S
P
O
S
P
O
S
P
O
27
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

28
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

29
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 enoughfor a
  while
• Lack of end-user facing killer apps
• Lots of misunderstanding to clear up

30
Crossing the Chasm

• Communicating the vision
• 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

31
Future Outlook

• 2007 2009
• 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

32
A Mainstream Application of the Semantic Web

33
Twine.com Overview

• Helps individuals and groups
• organize, share and discover
• information around their interests.
• Instead of social networking, its interest
  networking
• Twine is the best service for keeping up with
  your interests and sharing what interests you
  with other like-minded people.
• Twine will generate a large number of vertical
  interest portals and attract traffic from search
  engines and partners
• Twine will monetize through advertising,
  affiliate commerce, sponsorships and
  subscriptions

34
Positioning

• Facebook - For your relationships
• LinkedIn - For your career
• Twine - For your interests

35
Twine Compared to Other Tools

• Wikis
• Document centric, unstructured data
• Everyone sees the same view of the world
• Geared towards space (a directory of articles)
  rather than geared towards time (a sequence of
  articles)
• Users have to do all the work of linking and
  tagging
• No support for social networking or sharing
• Only for groups
• Blogs
• Document centric, unstructured data
• Document centric, unstructured data
• Everyone sees the same view of the world
• Geared towards time (a sequence of articles)
  rather than geared towards space (a directory of
  articles)
• Users have to do all the work of linking and
  tagging
• No support for social networking or sharing
• Only for individuals
• Twine
• Data centric all data (even unstructured data)
  becomes structured data

36
Twine is Smart
Automatically captures organizes info
Tags, crawls links related information
Emails
Bookmarks
Documents
Track Interests
People
Products
Collaborate Discuss
Photos
Videos
Search Discover
Places
Notes
Classifieds
Helps you navigate and search
Recommends relevant things
Events
37
Lets take a look at Twine(demo of Twine site)
38
Twine is Powered by The Semantic Web

• Twine is built on a new Semantic Web platform
• 15 patents pending and in process
• Easily create new Semantic Web apps
• Written in pure Java
• Anyone can add edit ontologies
• Scale to manage 10s of billions of RDF triples
• Developer tools and APIs
• Open up our platform APIs and open-source in the
  future
• Be the center of the Semantic Web ecosystem

39
Radar Networks Semantic Web Platform
Web App
Twine.com
Bookmarklet Email
User Portal
REST API SPARQL
RSS Feeds
Cache
AJAX, Jetty, PicoContainer, Java, XML, SPARQL
Jena, ATOM
KnowledgeBase
Object Query Cache
Class inferencing
Semantic Object
Tuple Query
Cache
Platform
RDF, OWL
Ontology
TupleStore service
SQL Query Generator
Predicate Inferencing
Access Control
Cache
Remote Access
RDF, OWL, SQL Mina
SQL Database
WebDAV File Store
Storage
Relational database
Flat File Store
Postgres, Solaris
webDAV, Isilon cluster
40
Differentiation

• Focused on sharing knowledge around interests,
  not just socializing
• Smarter than other sites Twine learns,
  organizes and recommends automatically
• Powered by the Semantic Web New capabilities
  are possible
• Unified place for all types of information

41
Target Customer

• Twine is for active users of the Web, including
  consumers and professionals, who create, find and
  share information about their interests

• Interests
• Professional associations
• Alumni groups
• Social networks (Facebook, Plaxo, LinkedIn)
• Volunteer organizations
• Groups based on interests (hobbies, health,
  sports, entertainment, culture, family,
  technology, user groups, etc.)
• Participating/working in teams at organizations
  of all sizes

• Demographics
• 18 45 years old
• Have many personal interests and hobbies
• Social connections are important family,
  friends, colleagues
• Americans with a household income of 100,000 or
  more
• Nearly 26 million such consumers used the
  Internet in August 2003, spending an average of
  27.6 hours online -- more than any other income
  segment.
• Consume an average of nearly 3,000 pages a month,
  almost 300 pages more than the average Internet
  user

42
Market Opportunities for Twine

• Individuals
• Individual consumers
• Individual professionals

• Groups, Teams and Communities
• Interest communities
• Support groups
• Content publishers
• Users groups
• Hobbyists
• Social groups
• Product communities
• Event communities
• Communities of practice

43
Contact Info

• Visit www.twine.com to sign up for the invite
  beta wait-list
• You can email me at nova_at_radarnetworks.com
• My blog is at http//www.mindingtheplanet.net
• Thanks!

44
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//creativecommo
  ns.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