Nova Spivack - Understanding the Semantic Web and Twine Talk

Loading...

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

0 comments

Post a comment

    Post a comment
    Embed Video
    Edit your comment Cancel

    3 Favorites

    Nova Spivack - Understanding the Semantic Web and Twine Talk - Presentation Transcript

    1. Nova Spivack CEO & Founder Radar Networks Making Sense of the Semantic Web
    2. About This Talk
      • Making sense of the semantic sector
      • How the Semantic Web works
      • 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 The social graph just connects people People Groups The semantic graph connects everything Emails Companies Products Services Web Pages Multimedia Documents Events Projects Activities Interests Places
    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 Connections between people Connections between Information Email Social Networking Groupware Javascript Weblogs Databases File Systems HTTP Keyword Search USENET Wikis Websites Directory Portals 2010 - 2020 Web 1.0 2000 - 2010 1990 - 2000 PC Era 1980 - 1990 RSS Widgets PC’s 2020 - 2030 Office 2.0 XML RDF SPARQL AJAX FTP IRC SOAP Mashups File Servers Social Media Sharing Lightweight Collaboration ATOM Web 3.0 Web 4.0 Semantic Search Semantic Databases Distributed Search Intelligent personal agents Java SaaS Web 2.0 Flash OWL HTML SGML SQL Gopher P2P The Web The PC Windows MacOS SWRL OpenID BBS MMO’s VR Semantic Web Intelligent Web The Internet Social Web Web OS
    6. Beyond the Limits of Keyword Search Amount of data Productivity of Search Databases 2010 - 2020 Web 1.0 2000 - 2010 1990 - 2000 PC Era 1980 - 1990 2020 - 2030 Web 3.0 Web 4.0 Web 2.0 The World Wide Web The Desktop Keyword search Natural language search Reasoning Tagging Semantic Search The Semantic Web The Intelligent Web Directories The Social Web Files & Folders
    7. Five Approaches to Semantics
      • Tagging
      • Statistics
      • Linguistics
      • Semantic Web
      • Artificial Intelligence
    8. 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
    9. 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
    10. 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…
    11. 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
    12. 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
    13. The Approaches Compared Make the software smarter Make the Data Smarter Statistics Linguistics Semantic Web A.I. Tagging
    14. 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
    15. In Practice: Hybrid Approach Works Best
        • Tagging
        • Semantic Web
        • Top-down
        • Statistics
        • Linguistics
        • Bottom-up
        • Artificial intelligence
    16. A Higher Resolution Web Coldplay Band Palo Alto City Jane Person IBM Company Dave Person Bob Person Design Team Group Stanford Alumnae Group IBM.com Web Site 123.JPG Photo Dave.com Weblog Sue Person Joe Person Dave.com RSS 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
    17. The Web IS the Database! Application A Application B Coldplay Band Palo Alto City Jane Person IBM Company Dave Person Bob Person Design Team Group Stanford Alumnae Group IBM.com Web Site 123.JPG Photo Dave.com Weblog Sue Person Joe Person Dave.com RSS 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
    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
    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
    20. 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
    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
    22. 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 Object Predicate
    23. 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
    24. RDBMS vs Triplestore S P O Person Table 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 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
    25. Merging Databases in RDF is Easy S P O S P O S P O
    26. The Growing Linked Data Universe Twine Yahoo Freebase Reuters OpenCalais
    27. The Growing Semantic Web Consumers Developers Online Services Applications
    28. 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
    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…?
    30. A Mainstream Application of the Semantic Web…
    31. Twine.com Overview
      • Organize. Share. Discover.
      • Around your interests
      • Using the Semantic Web
    32. What Can You Do With Twine?
      • Organize
        • Collect & manage your stuff
      • Share
        • Author & share content
        • Discuss & collaborate
      • Discover
        • Track Interests
        • Search & explore
        • Get recommendations
    33. Differentiation
      • Facebook - For your relationships
      • LinkedIn - For your career
      • Twine - For your interests
      • Twitter + Del.icio.us + Blogger?
    34. Twine is Smart All Kinds Of Content Share Discover Organize Semantic tagging Recommendations Semantic Search Semantic linking
    35. Let’s take a look at Twine… (demo of Twine site…)
    36. Radar Networks’ Semantic Web Platform SQL Database Web App KnowledgeBase Bookmarklet & Email User Portal REST API SPARQL Relational database RSS Feeds Object Query & Cache Class inferencing Semantic Object TupleStore service SQL Query Generator Predicate Inferencing Tuple Query 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 Cache Remote Access Cache Cache Twine.com Platform Storage Ontology
    37. 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
    38. 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
    39. Contact Info
      • Visit www.twine.com to sign up for the invite beta wait-list
      • You can email me at [email_address]
      • My blog is at http://www.mindingtheplanet.net
      • Thanks!
    40. 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

    + novaspivacknovaspivack, 2 years ago

    custom

    2455 views, 3 favs, 3 embeds more stats

    UPDATED version of my talk on understanding the Sem more

    More info about this document

    © All Rights Reserved

    Go to text version

    • Total Views 2455
      • 2365 on SlideShare
      • 90 from embeds
    • Comments 0
    • Favorites 3
    • Downloads 108
    Most viewed embeds
    • 79 views on http://www.doquangtu.net
    • 10 views on http://doquangtu.net
    • 1 views on http://itech.xpressmedia.vn

    more

    All embeds
    • 79 views on http://www.doquangtu.net
    • 10 views on http://doquangtu.net
    • 1 views on http://itech.xpressmedia.vn

    less

    Flagged as inappropriate Flag as inappropriate
    Flag as inappropriate

    Select your reason for flagging this presentation as inappropriate. If needed, use the feedback form to let us know more details.

    Cancel
    File a copyright complaint
    Having problems? Go to our helpdesk?

    Categories