Radar Networks

Nova SpivackCEO & FounderRadar Networks

Making Senseof the

Semantic Web

Radar Networks

About This Talk

• Making sense of the semantic sector

• How the Semantic Web works

• Future outlook

• Twine.com

• Q & A

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The Big Opportunity…

The social graph just connects people

People

Groups

The semantic graph connects everything

EmailsCompanies

Products

Services

Web Pages

Multimedia

Documents

Events

Projects

Activities

Interests

Places

And it uses richer semantics to enable:

Better search

More targeted ads

Smarter collaboration

Deeper integration

Richer content

Better personalization

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The third decade of the Web

• A period in time, not a technology…

• Enrich the structure of the Webo Improve the quality of search, collaboration, publishing,

advertisingo Enables applications to become more integrated and

intelligent

• Transform Web from fileserver to databaseo Semantic technologies will play a key role

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The Intelligence is in the Connections

Connections between people

Connections between Information

Email

Social Networking

Groupware

JavascriptWeblogs

Databases

File Systems

HTTPKeyword Search

USENET

Wikis

Websites

Directory Portals

2010 - 2020

Web 1.0

2000 - 2010

1990 - 2000

PC Era1980 - 1990

RSSWidgets

PC’s

2020 - 2030

Office 2.0

XML

RDF

SPARQLAJAX

FTP IRC

SOAP

Mashups

File Servers

Social Media Sharing

Lightweight Collaboration

ATOM

Web 3.0

Web 4.0

Semantic SearchSemantic Databases

Distributed Search

Intelligent personal agents

JavaSaaS

Web 2.0 Flash

OWL

HTML

SGML

SQLGopher

P2P

The Web

The PC

Windows

MacOS

SWRL

OpenID

BBS

MMO’s

VR

Semantic Web

Intelligent Web

The Internet

Social Web

Web OS

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Beyond the Limits of Keyword Search

Amount of data

Productivity of Search

Databases

2010 - 2020

Web 1.0 2000 - 2010

1990 - 2000

PC Era1980 - 1990

2020 - 2030

Web 3.0

Web 4.0

Web 2.0 The World Wide Web

The DesktopKeyword search

Natural language search

Reasoning

Tagging

Semantic Search

The Semantic Web

The Intelligent Web

Directories

The Social Web

Files & Folders

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Five Approaches to Semantics

• Tagging

• Statistics

• Linguistics

• Semantic Web

• Artificial Intelligence

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The Tagging Approach

• Proso Easy for users to add and

read tagso Tags are just stringso No algorithms or ontologies

to deal witho No technology to learn

• Conso Easy for users to add and

read tagso Tags are just stringso No algorithms or ontologies

to deal witho No technology to learn

• Technorati

• Del.icio.us

• Flickr

• Wikipedia

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The Statistical Approach

• Pros: o Pure mathematical

algorithmso Massively scaleableo Language independent

• Cons: o No understanding of the

contento Hard to craft good querieso Best for finding really

popular things – not good at finding needles in haystacks

o Not good for structured data

• Google

• Lucene

• Autonomy

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The Linguistic Approach

• Pros:o True language

understandingo Extract knowledge from texto Best for search for particular

facts or relationshipso More precise queries

• Cons:o Computationally intensiveo Difficult to scaleo Lots of errorso Language-dependent

• Powerset

• Hakia

• Inxight, Attensity, and others…

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The Semantic Web Approach

• Pros:o More precise querieso Smarter apps with less worko Not as computationally

intensiveo Share & link data between

appso Works for both unstructured

and structured data

• Cons:o Lack of toolso Difficult to scaleo Who makes all the

metadata?

• Radar Networks

• DBpedia Project

• Metaweb

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The Artificial Intelligence Approach

• Pros:o This is the holy grail!!!!o Approximates the expertise

and common sense reasoning ability of a human domain expert

o Reasoning / inferencing, discovery, automated assistance, learning and self-modification, question answering, etc.

• Cons:o This is the holy grail!!!!o Computationally intensiveo Hard to program and designo Takes a long time and a lot

of work to reach critical mass of knowledge

• Cycorp

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The Approaches Compared

Make the software smarter

Make the Data Smarter

Statistics

Linguistics

SemanticWeb

A.I.

Tagging

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Two Paths to Adding Semantics

• “Bottom-Up” (Classic)o Add semantic metadata to pages and databases all over the

Webo Every Website becomes semantico Everyone has to learn RDF/OWL

• “Top-Down” (Contemporary)o Automatically generate semantic metadata for vertical

domainso Create services that provide this as an overlay to non-

semantic Webo Nobody has to learn RDF/OWL

-- Alex Iskold

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In Practice: Hybrid Approach Works Best

Tagging Semantic Web Top-down Statistics Linguistics Bottom-up Artificial intelligence

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A Higher Resolution Web

ColdplayBand

Palo AltoCity

JanePerson

IBMCompany

DavePerson

BobPerson

DesignTeamGroup

StanfordAlumnae

Group

IBM.comWeb Site

123.JPGPhotoDave.com

Weblog

SuePerson

JoePerson

Dave.comRSS 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

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The Web IS the Database!

Application A Application B

ColdplayBand

Palo AltoCity

JanePerson

IBMCompany

DavePerson

BobPerson

DesignTeamGroup

StanfordAlumnae

Group

IBM.comWeb Site

123.JPGPhoto

Dave.comWeblog

SuePerson

JoePerson

Dave.comRSS 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

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Smart Data

• Smart Data is data that carries whatever is needed to make use of it:

• Software can become dumber and more generic, yet ultimately be smarter

• The smarts moves into the data itself rather than being hard-coded into the software

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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.

• Just-in-time data

• Applications can pull the schema for data only when the data is actually needed, rather than having to anticipate it

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The Semantic Web = Open database layer for the Web

UserProfiles

WebContent

DataRecords

Apps &Services

Ads &Listings

Open Data Mappings

Open Data Records

Open Rules

Open Ontologies

Open Query Interfaces

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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

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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 ObjectPredicate

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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

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RDBMS vs Triplestore

S P OPerson Table

f_namejimnovachrislew

ID001002003004

l_namewissnerspivackjonestucker

Colleagues Table

SRC-ID001001001001002002002002003003003003004004004004

TGT-ID001002003004001002003004001002003004001002003004

Subject Predicate Object001 isA Person001 firstName Jim001 lastName Wissner001 hasColleague 002002 isA Person002 firstName Nova002 lastName Spivack002 hasColleague 003003 isA Person003 firstName Chris003 lastName Jones003 hasColleague 004004 isA Person004 firstName Lew004 lastName Tucker

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Merging Databases in RDF is Easy

S P OS P O S P O

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The Growing Linked Data Universe

Twine Yahoo

FreebaseReuters

OpenCalais

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The Growing Semantic Web

Consumers Developers

Online Services

Applications

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Future Outlook

• 2007 – 2009o Early-Adoptiono A few killer apps emergeo Other apps start to integrate

• 2010 – 2020o Mainstream Adoptiono Semantics widely used in Web content and apps

• 2020 +o Next big cycle: Reasoning and A.I. o The Intelligent Webo The Web learns and thinks collectively

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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…?

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A Mainstream Application of the Semantic Web…

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Twine.com Overview

Organize. Share. Discover.

Around your interests

Using the Semantic Web

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What Can You Do With Twine?

• Organizeo Collect & manage your stuff

• Shareo Author & share contento Discuss & collaborate

• Discovero Track Interestso Search & exploreo Get recommendations

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Differentiation

• Facebook - For your relationships

• LinkedIn - For your career

• Twine - For your interests

Twitter + Del.icio.us + Blogger?

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AllKinds

Of Content

Share

Discover

Organize

Semantic tagging

Recommendations Semantic Search

Semantic linking

Twine is Smart

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Let’s take a look at Twine…

(demo of Twine site…)

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SQL Database

Web App

KnowledgeBase

Bookmarklet& Email

User Portal REST APISPARQL

Relational database

RSS Feeds

Object Query& Cache

Class inferencingSemantic Object

TupleStore service

SQL QueryGenerator

PredicateInferencing

TupleQuery

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

CacheRemoteAccess

Cache

CacheTwine.com

Platform

Storage

Ontology

Radar Networks’ Semantic Web Platform

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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

moreo 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.

o Consume an average of nearly 3,000 pages a month, almost 300 pages more than the average Internet user

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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

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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

Rights

• This presentation is licensed under the Creative Commons Attribution License.o 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