Spivack Blogtalk 2008

Slide 1: Making Sense of the Semantic Web Nova Spivack CEO & Founder Radar Networks Radar Networks 1

Slide 2: About This Talk • Making sense of the semantic sector • Making the Semantic Web more useable • Future outlook • Twine.com •Q & A Radar Networks 2

Slide 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 3

Slide 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 Radar Networks 4

Slide 5: A Higher Resolution Web IB M . c o m Joe L i v e s in W e b S it e P e rs o n P a lo A lt o IB M C it y Co mpa ny P u b l is h e r o f Fa n o f S u b s c r ib e r t o L i v e s in E m p lo y e e o f S ue Fa n o f Ja ne P e rs o n D a v e .c o m P e rs o n F r ie n d o f RS S Fe e d C o l d p la y Ba nd Me m b e r of D e p ic t io n o f M a r r ie d t o S o u rc e o f D e s ig n Me m b e r Te a m of G ro u p 12 3 .J P G D a v e .c o m Bob P h o to W e b lo g P e rs o n D e p ic t io n o f Me m b e r o f Me m b e r o f Au th o r o f S ta n fo rd Da ve A lu m n a e P e rs o n G ro u p Me m b e r o f Radar Networks 5

Slide 6: The Web IS the Database! Application A Application B IB M . c o m W e b S it e Jo e P e rs o n L iv e s in P a lo A lt o IB M C it y Co mp a ny P u b lis h e r o f Fa n o f S u b s c r ib e r t o L iv e s in E m p lo y e e o f S ue Ja ne P e rs o n D a v e .c o m P e rs o n Fa n o f RS S Fe e d C o ld p la y F r ie n d o f Ba nd Me m be r of D e p ic t io n o f D e s ig n M a r r ie d t o Te a m S o u rc e o f Gro up Me mb e r of 12 3 .J P G D a v e .c o m Bob P ho to W e b lo g P e rs o n D e p ic t io n o f Me mb e r o f S ta n fo rd Me m b e r o f Da ve Au t h o r o f A lu m n a e P e rs o n G ro u p Me m b e r o f Radar Networks 6

Slide 7: The Intelligence is in the Connections Intelligent Web Connections between Information Web OS Web 4.0 2020 - 2030 Intelligent personal agents Semantic Web Web 3.0 Distributed Search SWRL OWL 2010 - 2020 SPARQL Semantic Databases OpenID AJAX Semantic Search Social Web RSS ATOM 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 7

Slide 8: Beyond the Limits of Keyword Search Productivity of Search The Intelligent Web Web 4.0 2020 - 2030 Reasoning The Semantic Web Web 2020 2010 - 3.0 Semantic Search 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 8

Slide 9: 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 9

Slide 10: Five Approaches to Semantics • Tagging • Statistics • Linguistics • Semantic Web • Artificial Intelligence Radar Networks 10

Slide 11: The Tagging Approach • Pros • Technorati • Easy for users to add and read tags • Tags are just strings • Del.icio.us • No algorithms or ontologies to deal with • No technology to learn • Flickr • Cons • Wikipedia • Easy for users to add and read tags • Tags are just strings • No algorithms or ontologies to deal with • No technology to learn Radar Networks 11

Slide 12: The Statistical Approach • Pros: • Google • Pure mathematical algorithms • Massively scaleable • Lucene • Language independent • Autonomy • 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 Radar Networks 12

Slide 13: The Linguistic Approach • Pros: • Powerset • True language understanding • Extract knowledge from text • Hakia • Best for search for particular facts or relationships • More precise queries • Inxight, Attensity, and others… • Cons: • Computationally intensive • Difficult to scale • Lots of errors • Language-dependent Radar Networks 13

Slide 14: The Semantic Web Approach • Pros: • Radar Networks • More precise queries • Smarter apps with less work • DBpedia Project • Not as computationally intensive • Share & link data between apps • Metaweb • Works for both unstructured and structured data • Cons: • Lack of tools • Difficult to scale • Who makes all the metadata? Radar Networks 14

Slide 15: The Artificial Intelligence Approach • Pros: • Cycorp • 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 Radar Networks 15

Slide 16: The Approaches Compared Make the Data Smarter A.I. Semantic Web Linguistics Tagging Statistics Make the software smarter Radar Networks 16

Slide 17: 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 Radar Networks 17

Slide 18: In Practice: Hybrid Approach Works Best Tagging Semantic Web Top-down Statistics Linguistics Bottom-up Artificial intelligence Radar Networks 18

Slide 19: 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 Radar Networks 19

Slide 20: 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 Radar Networks 20

Slide 21: 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

Slide 22: 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

Slide 23: 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

Slide 24: 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

Slide 25: RDBMS vs Triplestore Person Table S P O 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 004 lew tucker 002 firstName Nova 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

Slide 26: Merging Databases in RDF is Easy S P O S P O S P O Radar Networks 26

Slide 27: 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 Radar Networks 27

Slide 28: 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… Radar Networks 28

Slide 29: 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 Radar Networks 29

Slide 30: 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 Radar Networks 30

Slide 31: 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 Radar Networks 31

Slide 32: A Mainstream Application of the Semantic Web… Radar Networks 32

Slide 33: 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 Radar Networks 33

Slide 34: Positioning • Facebook - For your relationships • LinkedIn - For your career • Twine - For your interests Radar Networks 34

Slide 35: 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 Radar Networks 35

Slide 36: Twine is Smart Automatically captures Tags, crawls & links & organizes info related information Emails Bookmarks Documents Track People Interests Products Photos Collaborate & Discuss Videos Places Search & Notes Discover Classifieds Events Recommends Helps you navigate relevant things and search Radar Networks 36

Slide 37: Let’s take a look at Twine… (demo of Twine site…) Radar Networks 37

Slide 38: 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 38

Slide 39: 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 Object Query Tuple Semantic Object Class inferencing Cache & Cache Query Ontology Platform RDF, OWL TupleStore service SQL Query Predicate Remote Access Control 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 39

Slide 40: 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 Radar Networks 40

Slide 41: 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 • 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 • Consume an average of nearly 3,000 pages a month, almost 300 pages more than the average Internet user Radar Networks 41

Slide 42: Market Opportunities for Twine Individuals Groups, Teams and Communities • Individual consumers • Interest communities • Individual professionals • Support groups • Content publishers • Users groups • Hobbyists • Social groups • Product communities • Event communities • Communities of practice • Customer support • Collaborative teams Radar Networks 42

Slide 43: 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 43

Slide 44: 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 Radar Networks 44