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Radar Networks
Nova SpivackCEO & FounderRadar Networks
Making Senseof the
Semantic Web
Radar Networks
About This Talk
• Making sense of the semantic sector
• How the Semantic Web works
• Future outlook
• Twine.com
• Q & A
Radar Networks
The Big Opportunity…
The social graph just connects people
People
Groups
The semantic graph connects everything
EmailsCompanies
Products
Services
Web Pages
Multimedia
Documents
Events
Projects
Activities
Interests
Places
And it uses richer semantics to enable:
Better search
More targeted ads
Smarter collaboration
Deeper integration
Richer content
Better personalization
Radar Networks
The third decade of the Web
• A period in time, not a technology…
• Enrich the structure of the Webo Improve the quality of search, collaboration, publishing,
advertisingo Enables applications to become more integrated and
intelligent
• Transform Web from fileserver to databaseo Semantic technologies will play a key role
Radar Networks
The Intelligence is in the Connections
Connections between people
Connections between Information
Social Networking
Groupware
JavascriptWeblogs
Databases
File Systems
HTTPKeyword Search
USENET
Wikis
Websites
Directory Portals
2010 - 2020
Web 1.0
2000 - 2010
1990 - 2000
PC Era1980 - 1990
RSSWidgets
PC’s
2020 - 2030
Office 2.0
XML
RDF
SPARQLAJAX
FTP IRC
SOAP
Mashups
File Servers
Social Media Sharing
Lightweight Collaboration
ATOM
Web 3.0
Web 4.0
Semantic SearchSemantic Databases
Distributed Search
Intelligent personal agents
JavaSaaS
Web 2.0 Flash
OWL
HTML
SGML
SQLGopher
P2P
The Web
The PC
Windows
MacOS
SWRL
OpenID
BBS
MMO’s
VR
Semantic Web
Intelligent Web
The Internet
Social Web
Web OS
Radar Networks
Beyond the Limits of Keyword Search
Amount of data
Productivity of Search
Databases
2010 - 2020
Web 1.0 2000 - 2010
1990 - 2000
PC Era1980 - 1990
2020 - 2030
Web 3.0
Web 4.0
Web 2.0 The World Wide Web
The DesktopKeyword search
Natural language search
Reasoning
Tagging
Semantic Search
The Semantic Web
The Intelligent Web
Directories
The Social Web
Files & Folders
Radar Networks
Five Approaches to Semantics
• Tagging
• Statistics
• Linguistics
• Semantic Web
• Artificial Intelligence
Radar Networks
The Tagging Approach
• Proso Easy for users to add and
read tagso Tags are just stringso No algorithms or ontologies
to deal witho No technology to learn
• Conso Easy for users to add and
read tagso Tags are just stringso No algorithms or ontologies
to deal witho No technology to learn
• Technorati
• Del.icio.us
• Flickr
• Wikipedia
Radar Networks
The Statistical Approach
• Pros: o Pure mathematical
algorithmso Massively scaleableo Language independent
• Cons: o No understanding of the
contento Hard to craft good querieso Best for finding really
popular things – not good at finding needles in haystacks
o Not good for structured data
• Lucene
• Autonomy
Radar Networks
The Linguistic Approach
• Pros:o True language
understandingo Extract knowledge from texto Best for search for particular
facts or relationshipso More precise queries
• Cons:o Computationally intensiveo Difficult to scaleo Lots of errorso Language-dependent
• Powerset
• Hakia
• Inxight, Attensity, and others…
Radar Networks
The Semantic Web Approach
• Pros:o More precise querieso Smarter apps with less worko Not as computationally
intensiveo Share & link data between
appso Works for both unstructured
and structured data
• Cons:o Lack of toolso Difficult to scaleo Who makes all the
metadata?
• Radar Networks
• DBpedia Project
• Metaweb
Radar Networks
The Artificial Intelligence Approach
• Pros:o This is the holy grail!!!!o Approximates the expertise
and common sense reasoning ability of a human domain expert
o Reasoning / inferencing, discovery, automated assistance, learning and self-modification, question answering, etc.
• Cons:o This is the holy grail!!!!o Computationally intensiveo Hard to program and designo Takes a long time and a lot
of work to reach critical mass of knowledge
• Cycorp
Radar Networks
The Approaches Compared
Make the software smarter
Make the Data Smarter
Statistics
Linguistics
SemanticWeb
A.I.
Tagging
Radar Networks
Two Paths to Adding Semantics
• “Bottom-Up” (Classic)o Add semantic metadata to pages and databases all over the
Webo Every Website becomes semantico Everyone has to learn RDF/OWL
• “Top-Down” (Contemporary)o Automatically generate semantic metadata for vertical
domainso Create services that provide this as an overlay to non-
semantic Webo Nobody has to learn RDF/OWL
-- Alex Iskold
Radar Networks
In Practice: Hybrid Approach Works Best
Tagging Semantic Web Top-down Statistics Linguistics Bottom-up Artificial intelligence
Radar Networks
A Higher Resolution Web
ColdplayBand
Palo AltoCity
JanePerson
IBMCompany
DavePerson
BobPerson
DesignTeamGroup
StanfordAlumnae
Group
IBM.comWeb Site
123.JPGPhotoDave.com
Weblog
SuePerson
JoePerson
Dave.comRSS Feed
Lives in
Publisher of
Friend of
Depiction of
Depiction of
Member of
Married to
Member of
Member of
Member of
Fan of
Lives in
Subscriber to
Source of
Author of
Member of
Employee of
Fan of
Radar Networks
The Web IS the Database!
Application A Application B
ColdplayBand
Palo AltoCity
JanePerson
IBMCompany
DavePerson
BobPerson
DesignTeamGroup
StanfordAlumnae
Group
IBM.comWeb Site
123.JPGPhoto
Dave.comWeblog
SuePerson
JoePerson
Dave.comRSS Feed
Lives in
Publisher of
Friend of
Depiction of
Depiction of
Member of
Married to
Member of
Member of
Member of
Fan of
Lives in
Subscriber to
Source of
Author of
Member of
Employee of
Fan of
Radar Networks
Smart Data
• 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
Radar Networks
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.
• Just-in-time data
• Applications can pull the schema for data only when the data is actually needed, rather than having to anticipate it
Radar Networks
The Semantic Web = Open database layer for the Web
UserProfiles
WebContent
DataRecords
Apps &Services
Ads &Listings
Open Data Mappings
Open Data Records
Open Rules
Open Ontologies
Open Query Interfaces
Radar Networks
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
Radar Networks
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
Subject ObjectPredicate
Radar Networks
Semantic Web Data is Self-Describing Linked Data
Data Record ID
Field 1 Value
Field 2 Value
Field 3 Value
Field 4 Value
Definition
Definition
Definition
Definition
Definition
Definition
Definition
Ontologies
Radar Networks
RDBMS vs Triplestore
S P OPerson Table
f_namejimnovachrislew
ID001002003004
l_namewissnerspivackjonestucker
Colleagues Table
SRC-ID001001001001002002002002003003003003004004004004
TGT-ID001002003004001002003004001002003004001002003004
Subject Predicate Object001 isA Person001 firstName Jim001 lastName Wissner001 hasColleague 002002 isA Person002 firstName Nova002 lastName Spivack002 hasColleague 003003 isA Person003 firstName Chris003 lastName Jones003 hasColleague 004004 isA Person004 firstName Lew004 lastName Tucker
Radar Networks
Merging Databases in RDF is Easy
S P OS P O S P O
Radar Networks
The Growing Linked Data Universe
Twine Yahoo
FreebaseReuters
OpenCalais
Radar Networks
The Growing Semantic Web
Consumers Developers
Online Services
Applications
Radar Networks
Future Outlook
• 2007 – 2009o Early-Adoptiono A few killer apps emergeo Other apps start to integrate
• 2010 – 2020o Mainstream Adoptiono Semantics widely used in Web content and apps
• 2020 +o Next big cycle: Reasoning and A.I. o The Intelligent Webo The Web learns and thinks collectively
Radar Networks
The Future of the Platform…
• 1980’s -- The Desktop is the platform
• 1990’s -- The Browser / Server is the platform
• 2000’s -- Web Services are the platform
• 2010’s -- The Semantic Web is the platform
• 2020’s -- The WebOS is the platform
• 2030’s -- The Human Body is the platform…?
Radar Networks
A Mainstream Application of the Semantic Web…
Radar Networks
Twine.com Overview
Organize. Share. Discover.
Around your interests
Using the Semantic Web
Radar Networks
What Can You Do With Twine?
• Organizeo Collect & manage your stuff
• Shareo Author & share contento Discuss & collaborate
• Discovero Track Interestso Search & exploreo Get recommendations
Radar Networks
Differentiation
• Facebook - For your relationships
• LinkedIn - For your career
• Twine - For your interests
Twitter + Del.icio.us + Blogger?
Radar Networks
AllKinds
Of Content
Share
Discover
Organize
Semantic tagging
Recommendations Semantic Search
Semantic linking
Twine is Smart
Radar Networks
Let’s take a look at Twine…
(demo of Twine site…)
Radar Networks
SQL Database
Web App
KnowledgeBase
Bookmarklet& Email
User Portal REST APISPARQL
Relational database
RSS Feeds
Object Query& Cache
Class inferencingSemantic Object
TupleStore service
SQL QueryGenerator
PredicateInferencing
TupleQuery
Access Control
WebDAV File Store
Flat File Store
AJAX, Jetty, PicoContainer, Java, XML, SPARQL Jena, ATOM
RDF, OWL
RDF, OWL, SQL Mina
Postgres, Solaris
webDAV, Isilon cluster
CacheRemoteAccess
Cache
CacheTwine.com
Platform
Storage
Ontology
Radar Networks’ Semantic Web Platform
Radar Networks
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
moreo 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.
o Consume an average of nearly 3,000 pages a month, almost 300 pages more than the average Internet user
Radar Networks
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
• Customer support
• Collaborative teams
Radar Networks
Contact Info
• Visit www.twine.com to sign up for the invite beta wait-list
• You can email me at [email protected]
• My blog is at http://www.mindingtheplanet.net
• Thanks!
Radar Networks
Rights
• This presentation is licensed under the Creative Commons Attribution License.o 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