Some really really smart people, in Nwokedi’s area, are working on how to keep web search engines going as the amount of web-based data continues to explode. Nova Spivack of Radar Networks is at the head of the pack. Take a spin through his slides; at least you’ll get an idea of what the term “Semantic Web” is all about.

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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 Better search Places Products More targeted ads Smarter collaboration Interests Services Deeper integration Web Pages Activities Richer content Documents Better personalization Projects Events Multimedia 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: The Intelligence is in the Connections Intelligent Web Connections between Information Web 4.0 Web OS 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 ATOM Widgets RSS P2P RDF Mashups Office 2.0 Web 2.0 Javascript Flash SOAP XML 2000 - 2010 Weblogs Social Media Sharing The Web Java HTML SaaS Social Networking HTTP Directory Portals Wikis VR Keyword Search Lightweight Collaboration Web 1.0 The PC BBS Gopher Websites 1990 - 2000 MMO’s MacOS SQL Groupware SGML Databases Windows File Servers The Internet PC Era IRC Email 1980 - 1990 FTP USENET PC’s File Systems Connections between people Radar Networks 5

Slide 6: Beyond the Limits of Keyword Search Productivity of Search The Intelligent Web Web 4.0 2020 - 2030 Reasoning The Semantic Web Web 20203.0 Semantic Search 2010 - The Social Web Natural language search Web2010 2.0 The World Wide Web 2000 - Tagging Web2000 1.0 1990 - Keyword search The Desktop Directories PC Era 1980 - 1990 Files & Folders Databases Amount of data Radar Networks 6

Slide 7: A Higher Resolution Web IBM.com Web Site Lives in Joe Person IBM Palo Alto Company City Publisher of Fan of Subscriber to Lives in Employee of Sue Person Jane Fan of Dave.com Person Friend of RSS Feed Coldplay Band Member of Depiction of Married to Design Source of Member Team of Group 123.JPG Photo Dave.com Bob Weblog Person Depiction of Member of Member of Author of Stanford Dave Alumnae Person Group Member of Radar Networks 7

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

Slide 9: 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 • Flickr • No technology to learn • Wikipedia • Cons 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 9

Slide 10: 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 10

Slide 11: The Linguistic Approach • Pros: • Powerset True language understanding • • Extract knowledge from text • Hakia • Best for search for particular facts or relationships • Inxight, Attensity, and others… • More precise queries • Cons: Computationally intensive • • Difficult to scale • Lots of errors • Language-dependent Radar Networks 11

Slide 12: 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 12

Slide 13: The Artificial Intelligence Approach • Pros: • Cycorp 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 Radar Networks 13

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

Slide 15: 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 15

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

Slide 17: 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 Radar Networks 17

Slide 18: 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 18

Slide 19: 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 19

Slide 20: 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 20

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

Slide 22: RDBMS vs Triplestore Person Table SPO Subject Predicate Object 001 isA Person f_name ID l_name 001 firstName Jim jim 001 wissner 001 lastName Wissner nova 002 spivack 001 hasColleague 002 chris 003 jones 002 isA Person 002 firstName Nova lew 004 tucker 002 lastName Spivack 002 hasColleague 003 003 isA Person 003 firstName Chris Colleagues Table 003 lastName Jones 003 hasColleague 004 SRC-ID 004 isA Person TGT-ID 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 22

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

Slide 24: The Web IS the Database! Application A Application B IBM.com Web Site Joe Lives in Person IBM Palo Alto Company City Publisher of Fan of Subscriber to Lives in Employee of Sue Person Jane Person Dave.com Fan of RSS Feed Coldplay Friend of Band Membe r of Depiction of Design Married to Team Source of Member Group 123.JPG of Photo Dave.com Bob Weblog Person Depiction of Member of Stanford Member of Dave Alumnae Author of Person Group Member of Radar Networks 24

Slide 25: 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 25

Slide 26: 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 26

Slide 27: 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 27

Slide 28: 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 28

Slide 29: 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 29

Slide 30: 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…? Radar Networks 30

Slide 31: A Mainstream Application of the Semantic Web… Radar Networks 31

Slide 32: 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 Radar Networks 32

Slide 33: How Twine Works 1. Collect or author structured or unstructured information into Twine via email, 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 5. Helps you connect with other people & groups to grow and share knowledge webs around common interests Radar Networks 33

Slide 34: 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 Radar Networks 34

Slide 35: 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 35

Slide 36: 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 36