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Spivack Blogtalk 2008
 

Spivack Blogtalk 2008

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    Spivack Blogtalk 2008 Spivack Blogtalk 2008 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… 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
    • 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
    • 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
    • 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
    • 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
    • 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…?
    • 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:
        • 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
    • 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
    • Smart Data
      • Smart Data is data that carries whatever is needed to make use of it:
        • Definition of intended meaning and schema
        • Policies and permissions
        • Context (links, etc.)
        • History and schedule
        • Authenticity
        • Sentiment
        • Behavior (each piece of data may someday have its own rules and/or agent(s) that seek to move the data to where it is needed, connect it, maintain it, protect it, improve it, etc.)
      • Software can become dumber and more generice, yet ultimately be smarter – the smarts moves into the data itself rather than being hard-coded into the software
    • 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.
      • Data can be shared and linked more easily
      • Just-in-time schemas – applications can pull the schema for data only when the data is actually needed, rather than having to anticipate it
    • 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
    • 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
    • A Mainstream Application of the Semantic Web…
    • Twine.com Overview
      • Helps individuals and groups
      • organize, share and discover
      • information around their interests .
      • Instead of social networking, it’s interest networking
        • Twine is the best service for keeping up with your interests and sharing what interests you with other like-minded people.
        • Twine will generate a large number of vertical interest portals and attract traffic from search engines and partners
        • Twine will monetize through advertising, affiliate commerce, sponsorships and subscriptions
    • Positioning
      • Facebook - For your relationships
      • LinkedIn - For your career
      • Twine - For your interests
    • Twine Compared to Other Tools
      • Wikis
        • Document centric, unstructured data
        • Everyone sees the same view of the world
        • Geared towards space (a directory of articles) rather than geared towards time (a sequence of articles)
        • Users have to do all the work of linking and tagging
        • No support for social networking or sharing
        • Only for groups
      • Blogs
        • Document centric, unstructured data
        • Document centric, unstructured data
        • Everyone sees the same view of the world
        • Geared towards time (a sequence of articles) rather than geared towards space (a directory of articles)
        • Users have to do all the work of linking and tagging
        • No support for social networking or sharing
        • Only for individuals
      • Twine
        • Data centric – all data (even unstructured data) becomes structured data
        • Different views for people with different permissions
        • Geared towards spacetime – view documents by directory or sequence
        • Automates tagging, linking and discovery
        • Built in social networking and sharing
        • For individuals and groups – a unified framework
    • Twine is Smart Emails Bookmarks Documents Products Collaborate & Discuss Search & Discover Track Interests Automatically captures & organizes info Recommends relevant things Helps you navigate and search Tags, crawls & links related information People Photos Videos Places Classifieds Notes Events
    • Let’s take a look at Twine… (demo of Twine site…)
    • Twine is Powered by The Semantic Web
      • Twine is built on a new Semantic Web platform
        • 15 patents pending and in process
      • Easily create new Semantic Web apps
        • Written in pure Java
        • Anyone can add & edit ontologies
        • Scale to manage 10’s of billions of RDF triples
        • Developer tools and API’s
      • Open up our platform API’s and open-source in the future
        • Be the center of the Semantic Web ecosystem
    • 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
    • Differentiation
      • Focused on sharing knowledge around interests, not just socializing
      • Smarter than other sites – Twine learns, organizes and recommends automatically
      • Powered by the Semantic Web – New capabilities are possible
      • Unified place for all types of information
    • 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
    • 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
    • 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