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

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

3 Radar Networks The Big Opportunity… The social graph just connects people People Groups The semantic graph connects everything… sCompanies Products Services Web Pages Multimedia Documents Events Projects Activities Interests Places Better search More targeted ads Smarter collaboration Deeper integration Richer content Better personalization

4 Radar Networks 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 Radar Networks The Intelligence is in the Connections Connections between people Connections between Information Social Networking Groupware Javascript Weblogs Databases File Systems HTTP Keyword Search USENET Wikis Websites Directory Portals Web PC Era RSS Widgets PC’s 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 Radar Networks Beyond the Limits of Keyword Search Amount of data Productivity of Search Databases Web PC Era 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 Radar Networks 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 Member of Married to Member of Member of Fan of Lives in Subscriber to Source of Author of Member of Employee of Fan of

8 Radar Networks Five Approaches to Semantics Tagging Statistics Linguistics Semantic Web Artificial Intelligence

9 Radar Networks 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

10 Radar Networks 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

11 Radar Networks 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…

12 Radar Networks 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

13 Radar Networks 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

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

15 Radar Networks 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

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

17 Radar Networks 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

18 Radar Networks 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

19 Radar Networks 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

20 Radar Networks RDF “Triples” the subject, which is an RDF URI reference or a blank nodeRDF URI referenceblank node the predicate, which is an RDF URI referenceRDF URI reference the object, which is an RDF URI reference, a literal or a blank nodeRDF URI referenceliteral blank node Source: SubjectObject Predicate

21 Radar Networks Semantic Web Data is Self-Describing Linked Data Data Record ID Field 1 Value Field 2 Value Field 3 Value Field 4 Value Definition Ontologies

22 Radar Networks RDBMS vs Triplestore SPO Person Table f_name jim nova chris lew ID l_name wissner spivack jones tucker Colleagues Table SRC-ID TGT-ID SubjectPredicateObject 001isAPerson 001firstNameJim 001lastNameWissner 001hasColleague isAPerson 002firstNameNova 002lastNameSpivack 002hasColleague isAPerson 003firstNameChris 003lastNameJones 003hasColleague isAPerson 004firstNameLew 004lastNameTucker

23 Radar Networks Merging Databases in RDF is Easy SPO SPO SPO

24 Radar Networks The Web IS the Database! Application AApplication 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 Member of Married to Member of Member of Fan of Lives in Subscriber to Source of Author of Membe r of Employee of Fan of

25 Radar Networks 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

26 Radar Networks 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…

27 Radar Networks 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

28 Radar Networks 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

29 Radar Networks 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 Next big cycle: Reasoning and A.I. The Intelligent Web The Web learns and thinks collectively

30 Radar Networks The Future of the Platform… 1980’s -- The desktop is the platform 1990’s -- The browser is the platform 2000’s -- The Web is the platform 2010’s -- The Graph is the platform 2020’s -- The network is the platform 2030’s -- The body is the platform…?

31 Radar Networks A Mainstream Application of the Semantic Web…

32 Radar Networks 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

33 Radar Networks How Twine Works 1.Collect or author structured or unstructured information into Twine via , the Web or the desktop 2.Twine creates a knowledge web automatically Understands, tags & links information automatically Automatically does further research for you on the Web Organizes information automatically 3.Provides semantic search, discovery & interest tracking 4.Helps you connect with other people & groups to grow and share knowledge webs around common interests

34 Radar Networks 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

35 Radar Networks Contact Info Visit to sign up for the invite beta wait-listwww.twine.com You can me at My blog is at Thanks!

36 Radar Networks 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 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