Nova Spivack - Semantic Web Talk
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Nova Spivack - Semantic Web Talk Nova Spivack - Semantic Web Talk Presentation Transcript

  • Nova Spivack CEO & Founder Radar Networks Making Sense of the Semantic Web
  • About This Talk
    • Making sense of the semantic sector
    • Making the Semantic Web more useable
    • Future outlook
    • Twine.com
    • Q & A
  • The Big Opportunity… 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
  • 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
  • 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
  • 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
  • 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
  • Five Approaches to Semantics
    • Tagging
    • Statistics
    • Linguistics
    • Semantic Web
    • Artificial Intelligence
  • 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
  • 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
  • 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…
  • 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
  • The Artificial Intelligence Approach
    • Pros:
      • Smart in narrow domains
      • Answer questions intelligently
      • Reasoning and learning
    • Cons:
      • Computationally intensive
      • Difficult to scale
      • Extremely hard to program
      • Does not work well outside of narrow domains
      • Training takes a lot of work
    • Cycorp
  • The Approaches Compared Make the software smarter Make the Data Smarter Statistics Linguistics Semantic Web A.I. Tagging
  • 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
  • In Practice: Hybrid Approach Works Best
      • Tagging
      • Semantic Web
      • Top-down
      • Statistics
      • Linguistics
      • Bottom-up
      • Artificial intelligence
  • 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
    • Apps can become smarter with less work, because the data carries knowledge about what it is and how to use it
    • Data can be shared and linked more easily
  • 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
  • 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
  • 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
  • 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
  • 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
  • Merging Databases in RDF is Easy S P O S P O S P O
  • 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
  • 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
  • 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…
  • 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 enough…for a while
    • Lack of end-user facing killer apps
    • Lots of misunderstanding to clear up
  • 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
  • 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
  • The Future of the Platform…
    • 1980’s -- The desktop is the platform
    • 1990’s -- The browser is the platform
    • 2000’s -- The server is the platform
    • 2010’s -- The Web is the platform
    • 2020’s -- The network is the platform
    • 2030’s -- The body is the platform…?
  • A Mainstream Application of the Semantic Web…
  • What is Twine?
    • Twine is a new service for managing & sharing information on the Web
    • Works for content, knowledge, data, or any other kinds of information
    • Designed for individuals and groups that need a better way to organize, search, share and keep track of their information
  • How Twine Works
    • Collect or author structured or unstructured information into Twine via email, the Web or the desktop
    • Twine creates a knowledge web automatically
      • Understands, tags & links information automatically
      • Automatically does further research for you on the Web
      • Organizes information automatically
    • Provides semantic search, discovery & interest tracking
    • Helps you connect with other people & groups to grow and share knowledge webs around common interests
  • Use-Cases
    • Individuals
      • Collect & author information about interests
      • Share with your friends & colleagues
      • Find and discover things more relevantly
    • Groups & Teams
      • Manage content & knowledge related to common interests, goals, or activities
      • Leverage and contribute to collective intelligence
      • Collaborate more productively
  • 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!
  • 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