Explaining The Semantic Web

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Explaining The Semantic Web

  1. 1. Radar Networks Nova Spivack CEO & Founder Radar Networks Making Sense of the Semantic Web
  2. 2. About This Talk <ul><ul><li>Making sense of the semantic sector </li></ul></ul><ul><ul><li>How the Semantic Web works </li></ul></ul><ul><ul><li>Future outlook </li></ul></ul><ul><ul><li>Twine.com </li></ul></ul><ul><ul><li>Q & A </li></ul></ul>Radar Networks
  3. 3. The Big Opportunity … Radar Networks 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 And it uses richer semantics to enable: Better search More targeted ads Smarter collaboration Deeper integration Richer content Better personalization
  4. 4. The third decade of the Web <ul><ul><li>A period in time, not a technology … </li></ul></ul><ul><ul><li>Enrich the structure of the Web </li></ul></ul><ul><ul><ul><li>Improve the quality of search, collaboration, publishing, advertising </li></ul></ul></ul><ul><ul><ul><li>Enables applications to become more integrated and intelligent </li></ul></ul></ul><ul><ul><li>Transform Web from fileserver to database </li></ul></ul><ul><ul><ul><li>Semantic technologies will play a key role </li></ul></ul></ul>Radar Networks
  5. 5. The Intelligence is in the Connections Radar Networks 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. 6. Beyond the Limits of Keyword Search Radar Networks 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. 7. Five Approaches to Semantics <ul><ul><li>Tagging </li></ul></ul><ul><ul><li>Statistics </li></ul></ul><ul><ul><li>Linguistics </li></ul></ul><ul><ul><li>Semantic Web </li></ul></ul><ul><ul><li>Artificial Intelligence </li></ul></ul>Radar Networks
  8. 8. The Tagging Approach <ul><ul><li>Pros </li></ul></ul><ul><ul><ul><li>Easy for users to add and read tags </li></ul></ul></ul><ul><ul><ul><li>Tags are just strings </li></ul></ul></ul><ul><ul><ul><li>No algorithms or ontologies to deal with </li></ul></ul></ul><ul><ul><ul><li>No technology to learn </li></ul></ul></ul><ul><ul><li>Cons </li></ul></ul><ul><ul><ul><li>Easy for users to add and read tags </li></ul></ul></ul><ul><ul><ul><li>Tags are just strings </li></ul></ul></ul><ul><ul><ul><li>No algorithms or ontologies to deal with </li></ul></ul></ul><ul><ul><ul><li>No technology to learn </li></ul></ul></ul>Radar Networks <ul><ul><li>Technorati </li></ul></ul><ul><ul><li>Del.icio.us </li></ul></ul><ul><ul><li>Flickr </li></ul></ul><ul><ul><li>Wikipedia </li></ul></ul>
  9. 9. The Statistical Approach <ul><ul><li>Pros: </li></ul></ul><ul><ul><ul><li>Pure mathematical algorithms </li></ul></ul></ul><ul><ul><ul><li>Massively scaleable </li></ul></ul></ul><ul><ul><ul><li>Language independent </li></ul></ul></ul><ul><ul><li>Cons: </li></ul></ul><ul><ul><ul><li>No understanding of the content </li></ul></ul></ul><ul><ul><ul><li>Hard to craft good queries </li></ul></ul></ul><ul><ul><ul><li>Best for finding really popular things – not good at finding needles in haystacks </li></ul></ul></ul><ul><ul><ul><li>Not good for structured data </li></ul></ul></ul>Radar Networks <ul><ul><li>Google </li></ul></ul><ul><ul><li>Lucene </li></ul></ul><ul><ul><li>Autonomy </li></ul></ul>
  10. 10. The Linguistic Approach <ul><ul><li>Pros: </li></ul></ul><ul><ul><ul><li>True language understanding </li></ul></ul></ul><ul><ul><ul><li>Extract knowledge from text </li></ul></ul></ul><ul><ul><ul><li>Best for search for particular facts or relationships </li></ul></ul></ul><ul><ul><ul><li>More precise queries </li></ul></ul></ul><ul><ul><li>Cons: </li></ul></ul><ul><ul><ul><li>Computationally intensive </li></ul></ul></ul><ul><ul><ul><li>Difficult to scale </li></ul></ul></ul><ul><ul><ul><li>Lots of errors </li></ul></ul></ul><ul><ul><ul><li>Language-dependent </li></ul></ul></ul>Radar Networks <ul><ul><li>Powerset </li></ul></ul><ul><ul><li>Hakia </li></ul></ul><ul><ul><li>Inxight, Attensity, and others … </li></ul></ul>
  11. 11. The Semantic Web Approach <ul><ul><li>Pros: </li></ul></ul><ul><ul><ul><li>More precise queries </li></ul></ul></ul><ul><ul><ul><li>Smarter apps with less work </li></ul></ul></ul><ul><ul><ul><li>Not as computationally intensive </li></ul></ul></ul><ul><ul><ul><li>Share & link data between apps </li></ul></ul></ul><ul><ul><ul><li>Works for both unstructured and structured data </li></ul></ul></ul><ul><ul><li>Cons: </li></ul></ul><ul><ul><ul><li>Lack of tools </li></ul></ul></ul><ul><ul><ul><li>Difficult to scale </li></ul></ul></ul><ul><ul><ul><li>Who makes all the metadata? </li></ul></ul></ul>Radar Networks <ul><ul><li>Radar Networks </li></ul></ul><ul><ul><li>DBpedia Project </li></ul></ul><ul><ul><li>Metaweb </li></ul></ul>
  12. 12. The Artificial Intelligence Approach <ul><ul><li>Pros: </li></ul></ul><ul><ul><ul><li>This is the holy grail!!!! </li></ul></ul></ul><ul><ul><ul><li>Approximates the expertise and common sense reasoning ability of a human domain expert </li></ul></ul></ul><ul><ul><ul><li>Reasoning / inferencing, discovery, automated assistance, learning and self-modification, question answering, etc. </li></ul></ul></ul><ul><ul><li>Cons: </li></ul></ul><ul><ul><ul><li>This is the holy grail!!!! </li></ul></ul></ul><ul><ul><ul><li>Computationally intensive </li></ul></ul></ul><ul><ul><ul><li>Hard to program and design </li></ul></ul></ul><ul><ul><ul><li>Takes a long time and a lot of work to reach critical mass of knowledge </li></ul></ul></ul>Radar Networks <ul><ul><li>Cycorp </li></ul></ul>
  13. 13. The Approaches Compared Radar Networks Make the software smarter Make the Data Smarter Statistics Linguistics Semantic Web A.I. Tagging
  14. 14. Two Paths to Adding Semantics <ul><ul><li>“ Bottom-Up ” (Classic) </li></ul></ul><ul><ul><ul><li>Add semantic metadata to pages and databases all over the Web </li></ul></ul></ul><ul><ul><ul><li>Every Website becomes semantic </li></ul></ul></ul><ul><ul><ul><li>Everyone has to learn RDF/OWL </li></ul></ul></ul><ul><ul><li>“ Top-Down ” (Contemporary) </li></ul></ul><ul><ul><ul><li>Automatically generate semantic metadata for vertical domains </li></ul></ul></ul><ul><ul><ul><li>Create services that provide this as an overlay to non-semantic Web </li></ul></ul></ul><ul><ul><ul><li>Nobody has to learn RDF/OWL </li></ul></ul></ul><ul><ul><ul><ul><ul><li>-- Alex Iskold </li></ul></ul></ul></ul></ul>Radar Networks
  15. 15. In Practice: Hybrid Approach Works Best <ul><ul><li>Tagging </li></ul></ul><ul><ul><li>Semantic Web </li></ul></ul><ul><ul><li>Top-down </li></ul></ul><ul><ul><li>Statistics </li></ul></ul><ul><ul><li>Linguistics </li></ul></ul><ul><ul><li>Bottom-up </li></ul></ul><ul><ul><li>Artificial intelligence </li></ul></ul>Radar Networks
  16. 16. A Higher Resolution Web Radar Networks 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. 17. The Web IS the Database! Radar Networks 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. 18. Smart Data <ul><ul><li>Smart Data is data that carries whatever is needed to make use of it: </li></ul></ul><ul><ul><li>Software can become dumber and more generic, yet ultimately be smarter </li></ul></ul><ul><ul><li>The smarts moves into the data itself rather than being hard-coded into the software </li></ul></ul>Radar Networks
  19. 19. The Semantic Web is a Key Enabler <ul><ul><li>Moves the “ intelligence ” out of applications, into the data </li></ul></ul><ul><ul><li>Data becomes self-describing; Meaning of data becomes part of the data </li></ul></ul><ul><ul><li>Data = Metadata. </li></ul></ul><ul><ul><li>Just-in-time data </li></ul></ul><ul><ul><li>Applications can pull the schema for data only when the data is actually needed, rather than having to anticipate it </li></ul></ul>Radar Networks
  20. 20. The Semantic Web = Open database layer for the Web Radar Networks User Profiles Web Content Data Records Apps & Services Ads & Listings Open Data Mappings Open Data Records Open Rules Open Ontologies Open Query Interfaces
  21. 21. Semantic Web Open Standards <ul><ul><li>RDF – Store data as “ triples ” </li></ul></ul><ul><ul><li>OWL – Define systems of concepts called “ ontologies ” </li></ul></ul><ul><ul><li>Sparql – Query data in RDF </li></ul></ul><ul><ul><li>SWRL – Define rules </li></ul></ul><ul><ul><li>GRDDL – Transform data to RDF </li></ul></ul>Radar Networks
  22. 22. RDF “ Triples ” <ul><ul><li>the subject, which is an RDF URI reference or a blank node </li></ul></ul><ul><ul><li>the predicate, which is an RDF URI reference </li></ul></ul><ul><ul><li>the object, which is an RDF URI reference , a literal or a blank node </li></ul></ul>Radar Networks Source: http://www.w3.org/TR/rdf-concepts/#section-triples Subject Object Predicate
  23. 23. Semantic Web Data is Self-Describing Linked Data Radar Networks Data Record ID Field 1 Value Field 2 Value Field 3 Value Field 4 Value Definition Definition Definition Definition Definition Definition Definition Ontologies
  24. 24. RDBMS vs Triplestore Radar Networks 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. 25. Merging Databases in RDF is Easy Radar Networks S P O S P O S P O
  26. 26. The Growing Linked Data Universe Radar Networks Twine Yahoo Freebase Reuters OpenCalais
  27. 27. The Growing Semantic Web Radar Networks Consumers Developers Online Services Applications
  28. 28. Future Outlook <ul><ul><li>2007 – 2009 </li></ul></ul><ul><ul><ul><li>Early-Adoption </li></ul></ul></ul><ul><ul><ul><li>A few killer apps emerge </li></ul></ul></ul><ul><ul><ul><li>Other apps start to integrate </li></ul></ul></ul><ul><ul><li>2010 – 2020 </li></ul></ul><ul><ul><ul><li>Mainstream Adoption </li></ul></ul></ul><ul><ul><ul><li>Semantics widely used in Web content and apps </li></ul></ul></ul><ul><ul><li>2020 + </li></ul></ul><ul><ul><ul><li>Next big cycle: Reasoning and A.I. </li></ul></ul></ul><ul><ul><ul><li>The Intelligent Web </li></ul></ul></ul><ul><ul><ul><li>The Web learns and thinks collectively </li></ul></ul></ul>Radar Networks
  29. 29. The Future of the Platform … <ul><ul><li>1980 ’ s -- The Desktop is the platform </li></ul></ul><ul><ul><li>1990 ’ s -- The Browser / Server is the platform </li></ul></ul><ul><ul><li>2000 ’ s -- Web Services are the platform </li></ul></ul><ul><ul><li>2010 ’ s -- The Semantic Web is the platform </li></ul></ul><ul><ul><li>2020 ’ s -- The WebOS is the platform </li></ul></ul><ul><ul><li>2030 ’ s -- The Human Body is the platform … ? </li></ul></ul>Radar Networks
  30. 30. Radar Networks A Mainstream Application of the Semantic Web …
  31. 31. Twine.com Overview Organize. Share. Discover. Around your interests Using the Semantic Web Radar Networks
  32. 32. What Can You Do With Twine? <ul><ul><li>Organize </li></ul></ul><ul><ul><ul><li>Collect & manage your stuff </li></ul></ul></ul><ul><ul><li>Share </li></ul></ul><ul><ul><ul><li>Author & share content </li></ul></ul></ul><ul><ul><ul><li>Discuss & collaborate </li></ul></ul></ul><ul><ul><li>Discover </li></ul></ul><ul><ul><ul><li>Track Interests </li></ul></ul></ul><ul><ul><ul><li>Search & explore </li></ul></ul></ul><ul><ul><ul><li>Get recommendations </li></ul></ul></ul>Radar Networks
  33. 33. Differentiation Radar Networks <ul><ul><li>Facebook - For your relationships </li></ul></ul><ul><ul><li>LinkedIn - For your career </li></ul></ul><ul><ul><li>Twine - For your interests </li></ul></ul><ul><li>Twitter + Del.icio.us + Blogger? </li></ul>
  34. 34. Twine is Smart Radar Networks All Kinds Of Content Share Discover Organize Semantic tagging Recommendations Semantic Search Semantic linking
  35. 35. Let ’ s take a look at Twine … (demo of Twine site … ) Radar Networks
  36. 36. Radar Networks ’ Semantic Web Platform Radar Networks 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. 37. Target Customer Twine is for active users of the Web, including consumers and professionals, who create, find and share information about their interests Radar Networks <ul><li>Interests : </li></ul><ul><ul><li>Professional associations </li></ul></ul><ul><ul><li>Alumni groups </li></ul></ul><ul><ul><li>Social networks (Facebook, Plaxo, LinkedIn) </li></ul></ul><ul><ul><li>Volunteer organizations </li></ul></ul><ul><ul><li>Groups based on interests (hobbies, health, sports, entertainment, culture, family, technology, user groups, etc.) </li></ul></ul><ul><ul><li>Participating/working in teams at organizations of all sizes </li></ul></ul><ul><li>Demographics: </li></ul><ul><ul><li>18 – 45 years old </li></ul></ul><ul><ul><li>Have many personal interests and hobbies </li></ul></ul><ul><ul><li>Social connections are important – family, friends, colleagues </li></ul></ul><ul><ul><li>Americans with a household income of $100,000 or more </li></ul></ul><ul><ul><ul><li>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. </li></ul></ul></ul><ul><ul><ul><li>Consume an average of nearly 3,000 pages a month, almost 300 pages more than the average Internet user </li></ul></ul></ul>
  38. 38. Market Opportunities for Twine <ul><li>Individuals </li></ul><ul><ul><li>Individual consumers </li></ul></ul><ul><ul><li>Individual professionals </li></ul></ul>Radar Networks <ul><li>Groups, Teams and Communities </li></ul><ul><ul><li>Interest communities </li></ul></ul><ul><ul><li>Support groups </li></ul></ul><ul><ul><li>Content publishers </li></ul></ul><ul><ul><li>Users groups </li></ul></ul><ul><ul><li>Hobbyists </li></ul></ul><ul><ul><li>Social groups </li></ul></ul><ul><ul><li>Product communities </li></ul></ul><ul><ul><li>Event communities </li></ul></ul><ul><ul><li>Communities of practice </li></ul></ul><ul><ul><li>Customer support </li></ul></ul><ul><ul><li>Collaborative teams </li></ul></ul>
  39. 39. Contact Info <ul><ul><li>Visit www.twine.com to sign up for the invite beta wait-list </li></ul></ul><ul><ul><li>You can email me at [email_address] </li></ul></ul><ul><ul><li>My blog is at http://www.mindingtheplanet.net </li></ul></ul><ul><ul><li>Thanks! </li></ul></ul>Radar Networks
  40. 40. Rights <ul><ul><li>This presentation is licensed under the Creative Commons Attribution License. </li></ul></ul><ul><ul><ul><li>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. </li></ul></ul></ul><ul><ul><li>If you reproduce or redistribute in whole or in part, please give attribution to Nova Spivack, with a link to http://www.mindingtheplanet.net </li></ul></ul>Radar Networks

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