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Making
 Sense
 of the
Semantic
  Web


            Nova Spivack
           CEO & Founder
           Radar Networks

                     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

                                    The semantic graph connects everything
                       People

         Companies                   Emails               And it uses richer
                                                          semantics to enable:
    Places                                Products
                                                          Better search

 Interests                                     Services   More targeted ads

                                                          Smarter collaboration
 Activities                                   Web Pages
                                                          Deeper integration
     Projects                           Documents
                                                          Richer content

              Events             Multimedia               Better personalization
                       Groups


                                                                           Radar Networks
The third decade of the Web

• A period in time, not a technology…

• Enrich the structure of the Web
   o   Improve the quality of search, collaboration, publishing, advertising
   o   Enables applications to become more integrated and intelligent

• Transform Web from fileserver to database
   o   Semantic technologies will play a key role




                                                                     Radar Networks
The Intelligence is in the Connections

                                                                                                     Intelligent Web


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




                                                                                        Connections between people
                                                                                                                       Radar Networks
Beyond the Limits of Keyword Search


                                                                                     The Intelligent Web
                                                                                        Web 4.0
ctivity of Search                                                                         2020 - 2030          Reasoning

                                                                   The Semantic Web
                                                                     Web 3.0                    Semantic Search
                                                                       2010 - 2020

                                                    The Social Web
                                                                                     Natural language search

                       The World Wide Web
                                                     Web2010
                                                      2000 -
                                                             2.0
                                                                         Tagging
                           Web2000
                            1990 -
                                   1.0
                                              Keyword search
        The Desktop
                                Directories
        PC Era
         1980 - 1990
                       Files & Folders


               Databases




                                                                                                           Amount of data
                                                                                                                      Radar Networks
Five Approaches to Semantics


• Tagging

• Statistics

• Linguistics

• Semantic Web

• Artificial Intelligence




                               Radar Networks
The Tagging Approach

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




                                                      Radar Networks
The Statistical Approach

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




                                                    Radar Networks
The Linguistic Approach

• Pros:                               • Powerset
   o True language understanding
   o Extract knowledge from text      • Hakia
   o Best for search for particular
     facts or relationships           • Inxight, Attensity, and others…
   o More precise queries


• Cons:
   o   Computationally intensive
   o   Difficult to scale
   o   Lots of errors
   o   Language-dependent




                                                                Radar Networks
The Semantic Web Approach

• Pros:                                   • Radar Networks
   o   More precise queries
   o   Smarter apps with less work        • DBpedia Project
   o   Not as computationally intensive
   o   Share & link data between apps     • Metaweb
   o   Works for both unstructured and
       structured data

• Cons:
   o   Lack of tools
   o   Difficult to scale
   o   Who makes all the metadata?




                                                              Radar Networks
The Artificial Intelligence Approach

• Pros:                                 • Cycorp
   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 intensive
   o   Hard to program and design
   o   Takes a long time and a lot of
       work to reach critical mass of
       knowledge




                                                   Radar Networks
The Approaches Compared

Make the Data Smarter

                                             A.I.



                           Semantic
                             Web
                                      Linguistics

      Tagging
                  Statistics




                               Make the software smarter
                                                    Radar Networks
Two Paths to Adding Semantics

• “Bottom-Up” (Classic)
   o   Add semantic metadata to pages and databases all over the Web
   o   Every Website becomes semantic
   o   Everyone has to learn RDF/OWL

• “Top-Down” (Contemporary)
   o   Automatically generate semantic metadata for vertical domains
   o   Create services that provide this as an overlay to non-semantic Web
   o   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

                                                                                                                                IBM.com
                                                                                                                                Web Site
                               Joe
                              Person            Lives in       Palo Alto                           IBM
                                                                 City                            Company

                                                                                                                 Publisher of

                                       Fan of
       Subscriber to                                                          Lives in
                                                                                                Employee of
                                                                                                                                 Sue
                                                                                      Jane                                      Person
       Dave.com                                                                      Person
       RSS Feed                                               Fan of
                                  Coldplay
                                   Band                                                                   Friend of

                                                                  Member of

                                                                                                                                     Depiction of
                                                              Design                              Married to
  Source of                                                    Team           Member
                                                              Group                                                                   123.JPG
                                                                                of
              Dave.com                                                                                  Bob                            Photo
               Weblog                                                                                  Person

                                                                                                                      Depiction of
                                                           Member of

                                            Dave                               Stanford       Member of
                  Author of                Person                              Alumnae
                                                                                Group

                                                              Member of




                                                                                                                                           Radar Networks
The Web IS the Database!


       Application A                                                                                                                                                                             Application B




                                                                                                                                                                         IBM.com
                                                                                                                                                                         Web Site
                                                      Joe
                                                     Person                                                                           IBM
                                                                                         Palo Alto
                                                                         Lives in          City                                     Company



                                                                                                                                                        Publisher of

                                                               Fan of
                              Subscriber to                                                                 Lives in

                                                                                                                                  Employee of                             Sue
                                                                                                                        Jane                                             Person
                              Dave.com                                                                                 Person
                              RSS Feed                        Coldplay
                                                                                       Fan of
                                                               Band
                                                                                                                                            Friend of
                                                                                                Member of


                                                                                       Design                                                                                     Depiction of
                                                                                       Team                                          Married to
                                                                                       Group
                       Source of                                                                            Member                                                                123.JPG
                                                                                                              of                                                                   Photo
                                   Dave.com                                                                                                  Bob
                                    Weblog                                                                                                  Person


                                                                                                                                                               Depiction of
                                                                                    Member of

                                                                     Dave                                     Stanford
                                                                    Person                                    Alumnae           Member of
                                         Author of                                                             Group



                                                                                        Member 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


            User        Web       Ads &      Data     Apps &
           Profiles    Content   Listings   Records   Services




                      Open Query Interfaces

                      Open Data Mappings

                       Open Data Records

                            Open Rules

                         Open Ontologies
                                                                 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”


                          Predicate
     Subject                                                    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


                                                                                      Radar Networks
Semantic Web Data is Self-Describing Linked Data

Ontologies                               Definition


                                         Definition
 Definition
                                         Definition



                  Data Record ID
 Definition
                  Field 1 Value

                  Field 2 Value

 Definition       Field 3 Value

                  Field 4 Value

 Definition




                                                      Radar Networks
RDBMS vs Triplestore

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




                                                                           Radar Networks
Merging Databases in RDF is Easy

S PO                S P O          S PO




                                          Radar Networks
The Growing Linked Data Universe


      Twine                         Yahoo




                                   Freebase
  Reuters
 OpenCalais




                                       Radar Networks
The Growing Semantic Web

                      Online Services




Consumers                               Developers




                      Applications
                                        Radar Networks
Future Outlook

• 2007 – 2009
   o   Early-Adoption
   o   A few killer apps emerge
   o   Other apps start to integrate

• 2010 – 2020
   o   Mainstream Adoption
   o   Semantics widely used in Web content and apps

• 2020 +
   o   Next big cycle: Reasoning and A.I.
   o   The Intelligent Web
   o   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?

• Organize
  o   Collect & manage your stuff

• Share
  o   Author & share content
  o   Discuss & collaborate

• Discover
  o   Track Interests
  o   Search & explore
  o   Get recommendations




                                    Radar Networks
Differentiation


 • Facebook - For your relationships

 • LinkedIn - For your career

 • Twine - For your interests


Twitter + Del.icio.us + Blogger?




                                       Radar Networks
Twine is Smart

                 Semantic tagging   Semantic linking



                                                        Organize

     All
   Kinds
     Of                                                  Share
   Content


                                                        Discover




                 Recommendations      Semantic Search




                                                             Radar Networks
Let’s take a look at Twine…

  (demo of Twine site…)




                              Radar Networks
Radar Networks’ Semantic Web Platform


               Web App
 Twine.com         User Portal          REST API          Bookmarklet      RSS Feeds           Cache
                                        SPARQL              & Email

                                       AJAX, Jetty, PicoContainer, Java, XML, SPARQL Jena, ATOM

               KnowledgeBase
                 Semantic Object      Class inferencing      Object Query        Tuple          Cache
                                                              & Cache            Query

  Platform                                                                                     RDF, OWLOntology
               TupleStore service
                   SQL Query            Access Control        Predicate        Remote           Cache
                   Generator                                 Inferencing       Access

                                                                               RDF, OWL, SQL Mina


               SQL Database                                  WebDAV File Store
  Storage
                       Relational database                                   Flat File Store



                                 Postgres,                                 webDAV, Isilon
                                  Solaris                                     cluster




                                                                                                          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




Demographics:                                                         Interests:
 •   18 – 45 years old                                                  •    Professional associations
 •   Have many personal interests and hobbies                           •    Alumni groups
 •   Social connections are important – family, friends, colleagues     •    Social networks (Facebook, Plaxo, LinkedIn)
 •   Americans with a household income of $100,000 or more              •    Volunteer organizations
      o    Nearly 26 million such consumers used the Internet in        •    Groups based on interests (hobbies, health, sports,
           August 2003, spending an average of 27.6 hours online             entertainment, culture, family, technology, user groups, etc.)
           -- more than any other income segment.                       •    Participating/working in teams at organizations of all sizes
      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                    Groups, Teams and Communities

•   Individual consumers       •   Interest communities

                               •   Support groups
•   Individual professionals
                               •   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 nova@radarnetworks.com

• 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




                                                                                             Radar Networks

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Explaining The Semantic Web

  • 1. Making Sense of the Semantic Web Nova Spivack CEO & Founder Radar Networks Radar Networks
  • 2. About This Talk • Making sense of the semantic sector • How the Semantic Web works • Future outlook • Twine.com • Q&A Radar Networks
  • 3. The Big Opportunity… The social graph just connects people The semantic graph connects everything People Companies Emails And it uses richer semantics to enable: Places Products Better search Interests Services More targeted ads Smarter collaboration Activities Web Pages Deeper integration Projects Documents Richer content Events Multimedia Better personalization Groups Radar Networks
  • 4. The third decade of the Web • A period in time, not a technology… • Enrich the structure of the Web o Improve the quality of search, collaboration, publishing, advertising o Enables applications to become more integrated and intelligent • Transform Web from fileserver to database o Semantic technologies will play a key role Radar Networks
  • 5. The Intelligence is in the Connections Intelligent Web Web OS Web 4.0 2020 - 2030 Intelligent personal agents Semantic Web SWRL Web 3.0 Distributed Search OWL 2010 - 2020 OpenID AJAX SPARQL Semantic Databases between Information Social Web RSS ATOM Semantic Search Widgets P2P RDF Mashups Office 2.0 Javascript SOAP XML Flash Web 2.0 The Web Java 2000 - 2010 Weblogs Social Media Sharing HTML HTTP SaaS Social Networking Directory Portals Wikis VR Keyword Search Lightweight Collaboration The PC BBS Gopher Web 1.0 Websites 1990 - 2000 MMO’s MacOS SQL Groupware SGML Databases Windows File Servers The Internet PC Era FTP IRC Email 1980 - 1990 USENET PC’s File Systems Connections between people Radar Networks
  • 6. Beyond the Limits of Keyword Search The Intelligent Web Web 4.0 ctivity of Search 2020 - 2030 Reasoning The Semantic Web Web 3.0 Semantic Search 2010 - 2020 The Social Web Natural language search The World Wide Web Web2010 2000 - 2.0 Tagging Web2000 1990 - 1.0 Keyword search The Desktop Directories PC Era 1980 - 1990 Files & Folders Databases Amount of data Radar Networks
  • 7. Five Approaches to Semantics • Tagging • Statistics • Linguistics • Semantic Web • Artificial Intelligence Radar Networks
  • 8. The Tagging Approach • Pros • Technorati o Easy for users to add and read tags • Del.icio.us o Tags are just strings o No algorithms or ontologies to • Flickr deal with o No technology to learn • Wikipedia • Cons o Easy for users to add and read tags o Tags are just strings o No algorithms or ontologies to deal with o No technology to learn Radar Networks
  • 9. The Statistical Approach • Pros: • Google o Pure mathematical algorithms o Massively scaleable • Lucene o Language independent • Autonomy • Cons: o No understanding of the content o Hard to craft good queries o Best for finding really popular things – not good at finding needles in haystacks o Not good for structured data Radar Networks
  • 10. The Linguistic Approach • Pros: • Powerset o True language understanding o Extract knowledge from text • Hakia o Best for search for particular facts or relationships • Inxight, Attensity, and others… o More precise queries • Cons: o Computationally intensive o Difficult to scale o Lots of errors o Language-dependent Radar Networks
  • 11. The Semantic Web Approach • Pros: • Radar Networks o More precise queries o Smarter apps with less work • DBpedia Project o Not as computationally intensive o Share & link data between apps • Metaweb o Works for both unstructured and structured data • Cons: o Lack of tools o Difficult to scale o Who makes all the metadata? Radar Networks
  • 12. The Artificial Intelligence Approach • Pros: • Cycorp 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 intensive o Hard to program and design o Takes a long time and a lot of work to reach critical mass of knowledge Radar Networks
  • 13. The Approaches Compared Make the Data Smarter A.I. Semantic Web Linguistics Tagging Statistics Make the software smarter Radar Networks
  • 14. Two Paths to Adding Semantics • “Bottom-Up” (Classic) o Add semantic metadata to pages and databases all over the Web o Every Website becomes semantic o Everyone has to learn RDF/OWL • “Top-Down” (Contemporary) o Automatically generate semantic metadata for vertical domains o Create services that provide this as an overlay to non-semantic Web o Nobody has to learn RDF/OWL -- Alex Iskold Radar Networks
  • 15. In Practice: Hybrid Approach Works Best Tagging Semantic Web Top-down Statistics Linguistics Bottom-up Artificial intelligence Radar Networks
  • 16. A Higher Resolution Web IBM.com Web Site Joe Person Lives in Palo Alto IBM City Company Publisher of Fan of Subscriber to Lives in Employee of Sue Jane Person Dave.com Person RSS Feed Fan of Coldplay Band Friend of Member of Depiction of Design Married to Source of Team Member Group 123.JPG of Dave.com Bob Photo Weblog Person Depiction of Member of Dave Stanford Member of Author of Person Alumnae Group Member of Radar Networks
  • 17. The Web IS the Database! Application A Application B IBM.com Web Site Joe Person IBM Palo Alto Lives in City Company Publisher of Fan of Subscriber to Lives in Employee of Sue Jane Person Dave.com Person RSS Feed Coldplay Fan of Band Friend of Member of Design Depiction of Team Married to Group Source of Member 123.JPG of Photo Dave.com Bob Weblog Person Depiction of Member of Dave Stanford Person Alumnae Member of Author of Group Member of Radar Networks
  • 18. 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
  • 19. 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
  • 20. The Semantic Web = Open database layer for the Web User Web Ads & Data Apps & Profiles Content Listings Records Services Open Query Interfaces Open Data Mappings Open Data Records Open Rules Open Ontologies Radar Networks
  • 21. 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
  • 22. RDF “Triples” Predicate Subject 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 Radar Networks
  • 23. Semantic Web Data is Self-Describing Linked Data Ontologies Definition Definition Definition Definition Data Record ID Definition Field 1 Value Field 2 Value Definition Field 3 Value Field 4 Value Definition Radar Networks
  • 24. RDBMS vs Triplestore Person Table S PO Subject Predicate Object ID f_name l_name 001 isA Person 001 firstName Jim 001 jim wissner 001 lastName Wissner 002 nova spivack 001 hasColleague 002 003 chris jones 002 isA Person 002 firstName Nova 004 lew tucker 002 lastName Spivack 002 hasColleague 003 003 isA Person 003 firstName Chris Colleagues Table 003 lastName Jones 003 hasColleague 004 SRC-ID TGT-ID 004 isA Person 001 001 004 firstName Lew 001 002 004 lastName Tucker 001 003 001 004 002 001 002 002 002 003 002 004 003 001 003 002 003 003 003 004 004 001 004 002 004 003 004 004 Radar Networks
  • 25. Merging Databases in RDF is Easy S PO S P O S PO Radar Networks
  • 26. The Growing Linked Data Universe Twine Yahoo Freebase Reuters OpenCalais Radar Networks
  • 27. The Growing Semantic Web Online Services Consumers Developers Applications Radar Networks
  • 28. Future Outlook • 2007 – 2009 o Early-Adoption o A few killer apps emerge o Other apps start to integrate • 2010 – 2020 o Mainstream Adoption o Semantics widely used in Web content and apps • 2020 + o Next big cycle: Reasoning and A.I. o The Intelligent Web o The Web learns and thinks collectively Radar Networks
  • 29. 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
  • 30. A Mainstream Application of the Semantic Web… Radar Networks
  • 31. Twine.com Overview Organize. Share. Discover. Around your interests Using the Semantic Web Radar Networks
  • 32. What Can You Do With Twine? • Organize o Collect & manage your stuff • Share o Author & share content o Discuss & collaborate • Discover o Track Interests o Search & explore o Get recommendations Radar Networks
  • 33. Differentiation • Facebook - For your relationships • LinkedIn - For your career • Twine - For your interests Twitter + Del.icio.us + Blogger? Radar Networks
  • 34. Twine is Smart Semantic tagging Semantic linking Organize All Kinds Of Share Content Discover Recommendations Semantic Search Radar Networks
  • 35. Let’s take a look at Twine… (demo of Twine site…) Radar Networks
  • 36. Radar Networks’ Semantic Web Platform Web App Twine.com User Portal REST API Bookmarklet RSS Feeds Cache SPARQL & Email AJAX, Jetty, PicoContainer, Java, XML, SPARQL Jena, ATOM KnowledgeBase Semantic Object Class inferencing Object Query Tuple Cache & Cache Query Platform RDF, OWLOntology TupleStore service SQL Query Access Control Predicate Remote Cache Generator Inferencing Access RDF, OWL, SQL Mina SQL Database WebDAV File Store Storage Relational database Flat File Store Postgres, webDAV, Isilon Solaris cluster Radar Networks
  • 37. Target Customer Twine is for active users of the Web, including consumers and professionals, who create, find and share information about their interests Demographics: Interests: • 18 – 45 years old • Professional associations • Have many personal interests and hobbies • Alumni groups • Social connections are important – family, friends, colleagues • Social networks (Facebook, Plaxo, LinkedIn) • Americans with a household income of $100,000 or more • Volunteer organizations o Nearly 26 million such consumers used the Internet in • Groups based on interests (hobbies, health, sports, August 2003, spending an average of 27.6 hours online entertainment, culture, family, technology, user groups, etc.) -- more than any other income segment. • Participating/working in teams at organizations of all sizes o Consume an average of nearly 3,000 pages a month, almost 300 pages more than the average Internet user Radar Networks
  • 38. Market Opportunities for Twine Individuals Groups, Teams and Communities • Individual consumers • Interest communities • Support groups • Individual professionals • Content publishers • Users groups • Hobbyists • Social groups • Product communities • Event communities • Communities of practice • Customer support • Collaborative teams Radar Networks
  • 39. Contact Info • Visit www.twine.com to sign up for the invite beta wait-list • You can email me at nova@radarnetworks.com • My blog is at http://www.mindingtheplanet.net • Thanks! Radar Networks
  • 40. 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 Radar Networks