1Radar Networks

Nova SpivackCEO & FounderRadar Networks

Making Senseof the

Semantic Web

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About This Talk

•Making sense of the semantic sector

•Making the Semantic Web more useable

• 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

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 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

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The Intelligence is in the Connections

Connections between people

Con

nect

ions

bet

wee

n In

form

atio

n

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

Pro

duct

ivity

of S

earc

h

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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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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Five Approaches to Semantics

• Tagging

• Statistics

• Linguistics

• Semantic Web

•Artificial Intelligence

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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

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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

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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…

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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

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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

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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)• 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

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In Practice: Hybrid Approach Works Best

TaggingSemantic WebTop-downStatisticsLinguisticsBottom-upArtificial intelligence

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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

•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

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The Semantic Web = Open database layer for the Web

User

ProfilesWeb

ContentData

RecordsApps &

ServicesAds &

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 Web IS the Database!

Application A Application B

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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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

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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…

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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

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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

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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

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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…?

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A Mainstream Application of the Semantic Web…

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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

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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

4. Helps you connect with other people & groups to grow and share knowledge webs around common interests

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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

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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!

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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