STC Summit 2010: Semantic Web and Content Strategy

THERE’S NO SEMANTIC WEB
WITHOUT CONTENT AND DATA
MAY 5, 2010
TECHNICAL COMMUNICATION SUMMIT ’10
RACHEL LOVINGER
@RLOVINGER

© 2010 Razorfish. All rights reserved. Confidential and proprietary.

“Language is magic, and computers are still dumb."

- Aaron Straup Cope (flickr.com)

2 © 2010 Razorfish. All rights reserved. Confidential and proprietary.

BLACKBERRY


BLACKBERRY

Photo by enrique dans

BLACKBERRY

Photo by Rob MacEwen

AGENDA
‣ What is the semantic web?
‣ The key ingredients
‣ How it’s being used now
‣ What it means for Content Strategy


WHAT IS THE SEMANTIC WEB?


TRANSLATE THAT INTO COMPUTER-ESE

The underlying strategy of the
Semantic Web is to create
data and websites that are
“machine-readable.”

If machines comprehend
the meaning of data and
content, they can:
‣ manipulate data in more
meaningful ways
‣ provide precisely the
information that the user wants


IS THERE A STARBUCKS NEARBY?


A FRENCH RESTAURANT?


GIFT FOR YOUR SUPERHERO NIECE?

?
?
? ?
? ?
?
?

Photo by Brendan Riley

FIND A HAIR APPOINTMENT
Search for specific criteria:
• Highly-rated salon
• Near the office
• Available time that fits
your busy schedule


SOLVING FOR COMPLEXITY

Machines are good at complex things that people do poorly

• Computing or recalling long strings of numbers
• Comparing large sets of data
• Searching through millions of pages or data records for a
specific item

Image by Eric Dobbs

SOLVING FOR COMPLEXITY

People are good at some complex things that machines don’t handle well

Equivalence 6:00pm and 18:00

Lumping similar things 6:00pm and 8:23am

Splitting different things 6:07:10 and 060710

Semantic systems are designed to capture the logic that will allow them to
understand these types of relationships within data and use them to create new
facts about the data.


THE KEY INGREDIENTS


HOW DO MACHINES KNOW WHAT DATA MEANS?

Identity + Definition + Structure


IDENTITY + DEFINITION + STRUCTURE



IDs
‣ Machines need a unique, consistent way to identify a thing or concept.
‣ People can usually tell by context, but a machine needs a unique identifier to
be able to make connections or distinctions.

Bill Clinton = President Bush President Bush
President William Jefferson Clinton (George H. W.) (George W.)


IDENTITY: STANDARDS

Standard identifiers

ISBN: International Standard
Book Number
ISMN: Music
ISAN: Audiovisual works


IDENTITY: OPEN SOURCE

MusicBrainz: database of music metadata,
licensed by BBC to augment web pages

The Police MBID: 9e0e2b01-41db-4008-bd8b-988977d6019a



Ontology
Define classifications, properties, relationships, and logic

Blackberry1 is a type of Fruit
A Fruit is an Edible Thing

Blackberry2 is a type of Wireless E-mail Device
A Wireless E-mail Device is a Mobile Electronic Device

Properties of Edible Things:
Seasonal – Yes/No
Calories – #
Ingredients (optional) – other Edible Things

A Mobile Electronic Device can never be an Edible Thing.


Some non-standard ways to express semantics
‣ MicroFormats – uses XHTML & HTML markup to embed meaning in a webpage
‣ hCard for contact information
‣ hCalendar for events

This presentation was given
on 2010-04-16
at the Content Strategy Forum
in Paris, France.


‣ Machine Tags – definition added to simple user tagging (“folksonomy”)
‣ flora:tree=coniferous
‣ upcoming:event=81334


New Web Standards
Developed specifically for expressing metadata and metadata relationships
‣ Dublin Core – an ISO standard defining 15 common metadata elements
‣ RDF – a model for expressing metadata as triples (subject-predicate-object)
‣ OWL – adds semantic meaning
‣ SKOS – expresses structured controlled vocabularies, taxonomies

Object

Subject


DUBLIN CORE
METADATA INITIATIVE


DUBLIN CORE

A very flexible standard that defines 15 core metadata elements.
‣ contributor – An entity responsible for making contributions to the resource.
‣ coverage – The spatial or temporal topic of the resource, the spatial
applicability of the resource, or the jurisdiction under which the resource is
relevant.
‣ creator – An entity primarily responsible for making the resource.
‣ date – A point or period of time associated with an event in the lifecycle of the
resource.
‣ description – An account of the resource.
‣ format – The file format, physical medium, or dimensions of the resource.
‣ identifier – An unambiguous reference to the resource within a given context.


DUBLIN CORE CONTINUED

‣ language – A language of the resource.
‣ publisher – An entity responsible for making the resource available.
‣ relation – A related resource.
‣ rights – Information about rights held in and over the resource.
‣ source – A related resource from which the described resource is derived.
‣ subject – The topic of the resource.
‣ title – A name given to the resource.
‣ type – The nature or genre of the resource.


RDF – RESOURCE
DESCRIPTION FRAMEWORK


RDF

Purpose: To provide a structure (aka framework) for describing identified things
(aka resources)

Identified?
The thing you’re talking about must be identified in a unique way.
http://www.foaf.com/Person#RachelLovinger
http://www.allmovie.com/Actor#WillSmith

Note: URIs (uniform resource identifiers) look like URLs, but might not represent
an actual web page.


RDF

Composed of three basic elements
‣ Resources – the things being described
‣ Properties – the relationships between things
‣ Classes – the buckets used to group the things


RDF

The elements are combined to make simple statements in the form of Triples

<Subject> <Predicate> <Object>

RDF


Object

Subject


RDF


<Subject> <Predicate> <Object>

Example statement: “Men In Black stars Will Smith”

Example triple:
<MenInBlack> <hasStar> <WillSmith>

RDF

Information Expressed in Triples
<http://www.w3.org/2001/sw/RDFCore/ntriples/> <dc:creator> "Dave Beckett" .
<http://www.w3.org/2001/sw/RDFCore/ntriples/> <dc:creator> "Art Barstow" .
<http://www.w3.org/2001/sw/RDFCore/ntriples/> <dc:publisher> <http://www.w3.org/> .

Can also be expressed as XML
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:dc="http://purl.org/dc/elements/1.1/">
<rdf:Description rdf:about="http://www.w3.org/2001/sw/RDFCore/ntriples/">
<dc:creator>Art Barstow</dc:creator>
<dc:creator>Dave Beckett</dc:creator>
<dc:publisher rdf:resource="http://www.w3.org/"/>
</rdf:Description>
</rdf:RDF>

A SAMPLE OF RDF PROPERTIES

‣ type
‣ subClassOf
‣ subPropertyOf
‣ range
‣ domain
‣ label
‣ comment


RDF PROPERTIES

type – indicates that a resource belongs to a certain class

<WillSmith> <type> <Actor>

This defines which properties will be relevant to Will Smith.

RDF PROPERTIES

subClassOf – a class belongs to a parent class

<Actor> <subClassOf> <Person>

This means that all members of the Actor class are also members of the Person
class. All properties are inherited, and new properties specific to Actor can be
added.

<WillSmith> <type> <Actor>
Implies: <WillSmith> <type> <Person>

RDF PROPERTIES

subPropertyOf – a property has a parent property

<hasStar> <subPropertyOf> <hasActor>

This means that, if you make a statement using the hasStar property, a more
general statement using the hasActor property is also true.

Implies: <MenInBlack> <hasActor> <WillSmith>

RDF PROPERTIES

range & domain – the types of resources that use a property

<hasStar> <range> <Actor>
<hasStar> <domain> <Movie>

This means that, if you make a statement using the hasStar property, the system
will assume that the subject is a Movie and the object is an Actor.

<WillSmith> <hasStar> <MenInBlack>
is an untrue statement, but not invalid

RDF PROPERTIES

label – a human-readable name for a resource

<http://www.allmovie.com/Actor#WillSmith> <label>
<Will Smith>

RDF PROPERTIES

comment – a human-readable description

<http://www.pretendwebsite.com/rlovinger-semantic-
web.pdf> <comment> <A presentation that Rachel gave at
the Technical Communication Summit ‘10>

RDF: WEB OF TRIPLES

EdibleThing

Fruit

BerryPie

Blackberry1


RDF: WEB OF TRIPLES

EdibleThing

Fruit

BerryPie

IngredientOf
Blackberry1
abc 123
xyz


RDF: CONCLUSION

Why is RDF uniquely suited to expressing data and data relationships?

‣ More flexible – data relationships can be explored from all angles

‣ More efficient – large scale, data can be read more quickly
‣ not linear like a traditional database
‣ not hierarchical like XML


OWL – WEB ONTOLOGY
LANGUAGE


Winnie the Pooh and characters © A. A. Milne,, drawing by Ernest H. Shepard

OWL

Purpose: To develop ontologies that are compatible with the World Wide Web.

Ontologies?
Definition and classification of concepts and entities, and the relationships
between them.


OWL

Based on the basic elements of RDF; adds more vocabulary for describing
properties and classes. Allows the creation of rules that help further explain what
things mean.

‣ Relationships between classes
‣ Equality
‣ Richer properties
‣ Class property restrictions


OWL: RELATIONSHIPS BETWEEN CLASSES

‣ disjointWith – resources belonging to one class cannot belong to the other
<Person> <disjointWith> <Country>

‣ complementOf – the members of one class are all the resources that do not
belong to the other
<InanimateThings> <complementOf> <LivingThings>

OWL: EQUALITY

‣ sameAs – indicates that two resources actually refer to the same real-world
thing or concept
<wills> <sameAs> <wismith>

‣ equivalentClass – indicates that two classes have the same set of members
<CoopBoardMembers> <equivalentClass> <CoopResidents>

OWL: RICHER PROPERTIES

‣ Symmetric – a relationship between A and B is also true between B and A
<WillSmith> <marriedTo> <JadaPinkettSmith>
implies: <JadaPinkettSmith> <marriedTo> <WillSmith>

‣ Transitive – a relationship between A and B and between B and C is also true
between A and C
<piston> <isPartOf> <engine>
<engine> <isPartOf> <automobile>
implies: <piston> <isPartOf> <automobile>

OWL: RICHER PROPERTIES CONTINUED

‣ inverseOf – a relationship of type X between A and B implies a relationship of
type Y between B and A
<starsIn> <inverseOf> <hasStar>
implies: <WillSmith> <starsIn> <MenInBlack>

OWL: CLASS PROPERTY RESTRICTIONS

Define the members of a class based on their properties

‣ allValuesFrom – resources with properties that only have values that meet
this criteria
‣ Example: Property: hasParents, allValuesFrom: Human
‣ Resources that meet this criteria can be defined as also being members of
the Human class

‣ someValuesFrom – resources with properties that have at least one value
that meets criteria
‣ Example: Property: hasGraduated, someValuesFrom: College
‣ Resources that meet this criteria can be defined as being members of the
CollegeGraduates class


OWL: WEB OF TRIPLES PLUS LOGIC

EdibleThing

Fruit

BerryPie

IngredientOf
Blackberry1
abc 123
xyz

Note: Blackberry2 cannot be an ingredient of BerryPie, because it’s not an
EdibleThing and all ingredients of EdibleThings must also be EdibleThings


THIS SEEMS COMPLICATED. WHY DO IT?

These capabilities allow systems to express and make sense of first order logic.

All men are mortal
Socrates is a man
Therefore, Socrates is mortal


OWL: INFERENCING

‣ Create new triples based on existing triples
‣ Deduce new facts based on the stated facts

<piston> <isPartOf> <engine>
<engine> <isPartOf> <automobile>
implies: <piston> <isPartOf> <automobile>

OWL: THREE FLAVORS

‣ OWL Lite – uses a subset of the capabilities
‣ OWL DL – uses all the capabilities, but some are used in restricted ways
‣ OWL Full – unrestricted use of capabilities; no guarantee that all resulting
statements are valid


SKOS – SIMPLE KNOWLEDGE
ORGANIZATION SYSTEM


SKOS

‣ Also based on RDF
‣ Designed specifically to express information that’s more hierarchical – broader
terms, narrower terms, preferred terms and other thesaurus-like relationships
‣ Extendable into OWL, if needed


LINKED DATA


LINKED DATA: A DISTRIBUTED APPROACH

A Web of Data

Image by Richard Cyganiak and Anja Jentzsch


LINKED DATA: A DISTRIBUTED APPROACH

One page per concept
‣ URL is a type of ID
‣ “topic pages” – a powerful tool
and reference point
‣ high SEO value
‣ aggregate content
‣ contain related data & IDs


HOW IT’S BEING USED NOW


WALL STREET JOURNAL MOVIE REVIEWS


ENRICHED SEARCH RESULTS

Yahoo! SearchMonkey
Google Rich Snippets

+


ENRICHED VANITY SEARCH


GETGLUE – RATINGS AND RECOMMENDATIONS


THE NEW YORK TIMES – ALUMNI IN THE NEWS


BBC MUSIC BETA – ARTISTS PAGES


BBC PROGRAMME PAGES


DATA.GOV
“The purpose of Data.gov is to increase public access to high value,
machine readable datasets generated by the Executive Branch of the
Federal Government.”


FLUVIEW
National Flu Activity Map – a widget by CDC.gov


DATA.GOV.UK


DATA.GOV.UK APPS

Help you find things
‣ A post box
‣ A school
‣ An affordable place to live
‣ A job
‣ A volunteering opportunity
‣ A dentist
‣ A pharmacy
‣ A bike route
‣ A hospital
‣ A parking spot
Cyclestreets.net
‣ A care home


PARKOPEDIA


DATA.GOV.UK APPS

Get information on
‣ How taxes are spent ‣ Renewable energy projects
‣ Technology investments ‣ Planning Alerts
‣ Crime stats ‣ Anti-social behavior in the area
‣ The geological makeup of your area ‣ Hazardous street conditions
‣ Geographical details
‣ Local issues
‣ Local government
‣ Health
‣ Obesity
‣ Real Estate

fillthathole.org.uk

ASBOROMETER


WHAT IT MEANS FOR
CONTENT STRATEGY


Photo by Jon Higgins

SEMANTIC CAPABILITIES

Content Strategists should get familiar with these new kinds of tools and services
‣ Related Content Services
‣ Advanced Media Monitoring
‣ Semantic Publishing Tools
‣ Semantic Ad Targeting
‣ Rich Data Services
‣ Machine-Assisted Tagging
‣ Semantic SEO


RELATED CONTENT SERVICES

‣ Enhance existing pages
‣ Identify key concepts
‣ Place assets and information
on the page or link to relevant
offsite content
‣ Video, images, user-
generated reviews, tweets,
Wikipedia entries, etc.


RELATED CONTENT SERVICES

Example Services
Apture Provides additional contextual information in multimedia pop-ups, drawn
from places such as Wikipedia, YouTube and Flickr.

Evri Allows readers to browse articles, images, and videos related to the topic
of an article or content element, and provides widgets for sidebars, posts
and popovers.

Headup Provides contextually relevant material from social networks and web
services.

NewsCred Augments content with related stories from 6000 top news sources, as
well as topic pages and license-free photos.

Zemanta Suggests related content and pictures that editors can embed in articles
or blog posts.


ADVANCED MEDIA MONITORING

‣ Track Twitter, social
networks, blogs, discussion
boards, content sites
‣ Track a brand, industry,
domain or topic
‣ With semantic capabilities:
‣ more accurate relevance
‣ sentiment analysis
‣ Track ongoing stories and
audience reaction

Screenshot © 2010 Phase 2 Technology

ADVANCED MEDIA MONITORING

Example Services
Imooty Tracks keywords and mentions of a brand, using a simple dashboard or
by creating alerts, widgets, or RSS feeds.

Inbenta Follow the topics that people in your business are following.

Lexalytics Scans what’s being said in blogs, tweets and social media to provide
sentiment analysis about companies, topics and current events.

Tattler Mines news, websites, blogs, multimedia sites, and social media to find
mentions of topics or issues of interest to you.


SEMANTIC PUBLISHING TOOLS

‣ Content management tools that
incorporate a wide range of
structure and metadata capabilities
‣ Create and publish content
encoded with semantic markup
and meaningful metadata
‣ Not necessary to understand all
the underlying code
‣ Streamlines the publishing process
‣ Makes it faster, easier, and
cheaper to bring new content
products to market

Screenshot © 2010 Thomson Reuters

SEMANTIC PUBLISHING TOOLS

Example Services
OpenPublish A version of Drupal with OpenCalais machine assisted tagging and
RDFa formatting built in.

Jiglu Insight Finds hidden relationships to other content you’ve published and
automatically creates links.


SEMANTIC AD TARGETING

‣ Analyzing content pages for
message, context, or mood, and
inserts relevant ads
‣ Creates highly desirable ad inventory
‣ Audience targeting, without the
privacy concerns of behavioral
targeting
‣ Brand protection against unfortunate
term-matching
An example of non-semantic contextual ads


SEMANTIC AD TARGETING

Example Services
ad pepper Provides ad placement, lead generation and brand protection through
semantic analysis of page content and user behavior.

Peer39 Understands the meaning and sentiment of web pages so that ads can
be targeted to appropriate audiences, and also protects advertisers from
having their campaigns placed on negative or objectionable content.
Identifies hot topics on the fly, and quickly adapts to create new
“premium” inventory.

Proximic Performs real-time content analysis to accurately target ads, builds user
profiles for better audience targeting, and includes brand protection
measures.


RICH DATA SERVICES

‣ Enhance content with linked
data
‣ Import additional information,
assets, services, and user-
generated content
‣ Improve SEO
‣ Obtain additional data and
content for application
development
‣ Data set may already include
map to other desirable data
and services


RICH DATA SERVICES

Example Services
Factual An open data platform providing tools to enable anyone to contribute and
use sources of structured data.

Freebase An open, semantically enhanced database of information, similar to
Wikipedia, but with structured data on millions of topics in dozens of
domains.

iGlue A community editable database containing images, video, individuals,
institutions, and geographic locations.


MACHINE-ASSISTED TAGGING

‣ Streamlines the process of tagging content by extracting concepts on a page
‣ Suggests a set of consistent tags for each piece of content
‣ Content producer approves or rejects each suggested tag

Screenshot © 2010 Thomson Reuters

MACHINE-ASSISTED TAGGING

Example Services
OpenCalais Automatically tags people, places, companies, facts and events found in
the content.

TextWise Generates weighted, relevant metadata based on key concepts found in
the text of a document or web page.

Tagaroo An OpenCalais plug-in for WordPress.


SEMANTIC SEO

‣ Adds semantic markup to the content,
or validates existing markup
‣ Submits it to search engines
‣ Boosts search rankings
‣ Makes pages more accessible for
visually impaired users
‣ Displays additional business data,
content, or product information directly
in search results

Screenshot © 2010 Dapper

SEMANTIC SEO

Example Services
Google Rich Tests webpage markup to ensure that Google’s Rich Snippets feature
Snippets can interpret it correctly.
Testing Tool
Inbenta Assists in the creation of content using the terminology of popular
search queries.

Semantify Provides automated semantic enhancement of a site without changing
(by Dapper) its pages. Search engines see the site with RDFa tagging embedded
in the page.


ADDITIONAL RESOURCES

‣ RDF Primer (http://www.w3.org/TR/REC-rdf-syntax)
‣ OWL / Semantic Web (http://www.w3.org/2004/OWL)
‣ SKOS (http://www.w3.org/2004/02/skos)
‣ Dublin Core (http://dublincore.org)
‣ LinkedData.org – Resources from across the Linked Data community
‣ Sindice (http://sindice.com) – The semantic web index
‣ SchemaWeb (http://www.schemaweb.info) – A directory of RDF schemas
‣ Semantic Universe (http://www.semanticuniverse.com) – Educating the World
About Semantic Technologies and Applications)
‣ Semanticweb.org – A wiki for the semantic community
‣ ReadWriteWeb: Semantic Web Archives
(http://www.readwriteweb.com/archives/semantic-web/)
‣ Nimble – A Razorfish-Semantic Universe report coming out soon


CONCLUSION

‣ Content Strategy will still be needed to help implement and use these tools
‣ Related Content Services, Semantic Ad Targeting, Rich Data Services,
Semantic SEO, Taxonomy/Ontology/Controlled Vocabularies
‣ Establish business rules
‣ Help configure the tools
‣ Periodically monitor the results
‣ Make adjustments as needed
‣ Advanced Media Monitoring, Semantic Publishing Tools, Machine-Assisted
Tagging
‣ Ongoing interaction by insightful, skilled users
‣ CS might be the primary user
‣ CS might train others to get the best results from their use


QUESTIONS?

Rachel.Lovinger@razorfish.com
Twitter: @rlovinger
http://scattergather.razorfish.com

Thank you!


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