SEO Meets Semantic Web - Saint Patrick's Day 2015-Meetup

©2015 Eric Axel Franzon
SEO Meets Semantic Web
(Meets St. Patrick’s Day)
Welcome!

Eric Franzon
Managing Partner
Semantic Fuse
A Roadmap for SEO Today
and Tomorrow
SemanticWeb:

Semantic Web
is like the harmonica

Easy to play

Easy to play; takes work to master.

What we’ll discuss
• What is Semantic Web?
• Who’s using it?
• What makes it work?

What Is Semantic Web?
• A Web-scale architecture
• A metadata technology
• A layer of meaning on the Web
• In use TODAY!

What Is it Not?
• A software package
• Something that will ever
be “done”
• A replacement for the
current Web

What Is it Not?
• Limited to the public WWW
• A pipe dream
• A silver bullet
• HAL 9000 or Skynet

©2015 Eric Axel FranzonIoT Enhancements by Eric Franzon
IoT

• Globally
• Inexpensively
• In Real-Time
(public)
World
Wide
Web
HTTP
HTML
Based on W3C Standards

• Globally
• Inexpensively
• In Real-Time
Behind the
Firewall
(public)
World
Wide
Web
HTTP
HTML

• Globally
• Inexpensively
• In Real-Time
Semantic
Web
RDF
SPARQL
OWL

• Globally
• Inexpensively
• In Real-Time
Behind the
Firewall
Semantic
Web
RDF
SPARQL
OWL

• to connect DATA
• to make information
interpretable by machines
Semantic Web Standards
are used…

Machine Interpretation
as the Web Evolves…

Web 1.0 – Linking Documents

Web 1.0
“I see: characters
+ formatting
+ images”
--my Computer

Web 2.0 – Linking People

Web 2.0
“I see: characters
+ formatting
+ images”
--my Computer

It’s hard to interpret meaning
when all you see are characters,
images, and formatting.
Context is critical.

Web 2.0 – Linking People
Web 3.0 – Linking Data

Title
Price
Format
Cover
Band

Title
Price
Format
Cover
Band
“I see: things
+ relationships.
This is about a
collection of
music.”

Q: What does “Linked Data” have
to do with Semantic Web?

A Quick word of disambiguation…
Semantic Web
- A vision for a web of data
Semantic Web Standards
- A specific set of standards
Linked Data
- One application area of those
standards

Semantic Web
Standards
Semantic
Web
Linked
Open
Data

Semantic Web
Standards
Semantic
Web
Linked
Open
Data
Linked
Data

Linking Open Data Project
May, 2007

September 2011

August 2014

Data from these trusted sources
is available for you
to use in your applications TODAY.
Data you can LINK to.

Semantic Data that is machine READABLE.
…and machine INTERPRETABLE!

Who’s Using Semantic
Web Standards?

• Healthcare / Life Sciences
• Financial Services
• Manufacturing / Retail
• Marketing, Advertising
• SEO/SEM
• Libraries
• Archives
• Museums
• Governments
Who’s Using Sem Web?

Who’s Using Sem Web?

What it looks like

• Activities
• Businesses
• Groups
• Organizations
• People
• Places
• Products and Entertainment
• Websites
Used to Describe

What it looks like
<meta property='og:image' content="http://ia.media-
imdb.com/images/M/MV5BMjA0MDYyNzczN15BMl5BanBnXkFtZTYwNjMzNjM
z._V1_.jpg" />
<meta property='og:type' content="actor" />
<meta property='fb:app_id' content='115109575169727' />
<meta property='og:title' content="Peter O'Toole" />
<meta property='og:site_name' content='IMDb' />
<meta property="og:description" content="Peter O'Toole,
Actor: Lawrence of Arabia. A leading man of prodigious
talents, Peter O'Toole was raised in Leeds, England, the son
of Constance Jane Eliot (Ferguson), a Scottish nurse, and
Patrick Joseph O'Toole, an Irish bookie. As a boy, he decided
to become a journalist, beginning as a newspaper copy boy.
Although he succeeded in becoming a reporter, he discovered
the theater and made his stage debut at 17. He served as a
radioman in ..." />

What is schema.org?
“…A collection of schemas, i.e., html tags,
that webmasters can use to markup their
pages in ways recognized by major search
providers.”

e.g. Product Markup

e.g. TV Episode Markup

e.g. Company

Based on a sample of 12 billion web pages:
• ~5 million domains (6% of domains)
• 15 billion entities
• 65 billion triples
• 2.5 billion pages (~21% of pages)
-Reported in an August 2014 SemTechBiz Keynote
by R. V. Guha, Google Fellow
Schema.org Adoption

A work in progress

Growing Up
• ~ 100 categories at launch in 2011
• ~1200 by Sept. 2014
• Bibliographic Relationships & Periodicals
(Sept. 2, 2014)
• Music, Video Games, Sports, breadcrumbs,
itemList (Dec. 11, 2014)
• VisualArtwork, Invoices (Feb. 5, 2015)
• Fixes to documentation, examples, &
general bugs (ongoing)

Growing Pains
• Immature tools for
• Publishing
• Parsing
• Evaluating
• Lack of understanding/Misinformation

• Incorrect Signals being sent
• Global companies showing as local
• Old data
• Entities mismatched to concepts
Feeling the Pain

When it does work, though…

What makes SemWeb work?

The Technologies of SemWeb
• Data
• Schemas
• Query Language

The Data Language
Resource
Description
Framework

“RDF is good for distributing data
across the Web and pretending
it’s in one place.”
-Dean Allemang,
Author, Semantic Web for the Working Ontologist

• to connect DATA
• to make it interpretable
by machines
RDF is used…
RDF is made up of triples!

1. By uniquely identifying THINGS
2. By uniquely identifying RELATIONSHIPS
3. By using TRIPLES
Machine Interpretable - How?

So, what’s a THING?

A THING is anything that can be uniquely
identified by a URI or a literal (string)
Me
My postal code
The White House
L.A. County’s sales tax rate
http://ericaxel.com/eric.rdf#me
http://www.city-data.com/zips/59801.html
Lat: 38.89859 Long: -77.035971
9.750 %
http://ericfranzon.com/harpcase.jpg

This is a collection of THINGS:
t_people
Name City State Post code
Bill Carlsbad CA 92008
Eric Missoula MT 59801

Who’s your daddy?

Is Father of

Is Father ofhttp://ericaxel.com/eric.rdf#me

<owl:ObjectProperty rdf:ID="isFather">
<rdfs:domain rdf:resource="#Person"/>
<rdfs:range rdf:resource="#Person"/>
</owl:ObjectProperty>
ns:isFather

Is Father of
ns:isFather

ns:isFather

3. By using TRIPLES
What’s a triple?

The Building block of RDF
The Triple

Triples? It’s Elementary! (School)
song has title.

song has title.
Relationship

Predicate
song has title.

song has title.
That is a Triple!

“This band recorded a song.”
“This recording is part of a collection.”
“This item has a barcode.”
“I like blues.”
“I like B.L.U.E.S.”
“This image can be used non-commercially.”
“My email address is eric.franzon@gmail.com.”
Triples? It’s Elementary!

Song Has Title “Title”
Eric Created Webpage
Image
Has License CC Non-
Commercial
Make Assertions
Subjects
Objects
Predicates

Song
Author Title
PublisherLyrics
A Simple Graph

The Trouble with Triples

Visualization of graph from Pharma space
- Cytoscape.org

Where does one store triples?
In a “triple store”

Where does one store triples?
• Native Semantic Web stores
• RDBMS databases
• As native files (.rdf)
• Woven into documents (RDFa)
• Generated on the fly

Just so you know…
There are many ways of representing RDF:
• RDF/XML
• N3
• JSON-LD
• N-Triples
• Turtle
• RDFa
• Microdata
• Microformats
Each has pros and cons, but they all connect
THINGS and RELATIONSHIPS into TRIPLES

The Schemata
Linked Data schemas consist of:
Your RDF relationships (predicates)
+
Relationship descriptions

SemWeb Schemata
id First Name Last Name
1 Barbara Starr
Schema
Data
Initial Schema
hasID
hasFirstName hasLastName
Barbara Starr1
owl:sameAs
hasSurname
Relationship
description

Choosing Relationships
• Reuse popular vocabularies
–FOAF (Friend-of-a-friend)
–Dublin Core (library/publisher metadata)
–SIOC (Semantically-Interlinked Online
Communities)
–Schema.org
• ...or make up your own!

1. Resource Description Framework Schema
(RDFS): Simple, hierarchical classes
2. Simple Knowledge Organization System
(SKOS): Port taxonomies to the Semantic Web
3. Web Ontology Language (OWL): Complex
logical relationships
Relationship Descriptions

Worldcat.org
• A project of the OCLC

Vocabulary Combination “in the wild”

The query language
SPARQL
Protocol
And
RDF
Query
Language
SPARQL

SPARQL allows us to:
• Pull values from structured & semi-structured data
• Explore data by querying unknown relationships
• Perform complex joins of disparate databases in a
single, simple query
• Transform RDF data from one vocabulary
to another
--Lee Feigenbaum, Cambridge Semantics

<hasDepiction>
Eric

<hasLicense>
<hasDepiction>
Eric

<hasLicense>
<hasDepiction>
<likes>
Eric

<hasLicense>
<hasDepiction>
<likes>
<likes>
<likes>
Eric

Chicago, Illinois
On the shores
of Lake
Michigan,
Chicago is one
of the major…
<hasLicense>
<wrote>
<hasDepiction>
<likes>
<likes>
<likes>
Eric
Bob

Chicago, Illinois
On the shores
of Lake
Michigan,
Chicago is one
of the major…
<hasLicense>
<wrote>
<isAbout>
<hasDepiction>
<likes>
<likes>
<likes>
Eric
Bob

Chicago, Illinois
On the shores
of Lake
Michigan,
Chicago is one
of the major…
<hasLicense>
<wrote>
<isAbout>
<livedIn>
<hasDepiction>
<likes>
<likes>
<likes>
Eric
Bob

Chicago, Illinois
On the shores
of Lake
Michigan,
Chicago is one
of the major…
<hasLicense>
<hasLicense> <wrote>
<isAbout>
<livedIn>
<hasDepiction>
<likes>
<likes>
<likes>
Eric
Bob

What can we ask of a system like this?

Chicago, Illinois
On the shores
of Lake
Michigan,
Chicago is one
of the major…
<hasLicense>
<isAbout>
<livedIn>
<hasDepiction>
<likes>
<likes>
<likes>
Bob
Eric

Chicago, Illinois
On the shores
of Lake
Michigan,
Chicago is one
of the major…
<hasLicense>
<isAbout>
<livedIn>
<hasDepiction>
<likes>
<likes>
<likes>
What does Eric Like?
Bob
Eric

Chicago, Illinois
On the shores
of Lake
Michigan,
Chicago is one
of the major…
<hasLicense>
<isAbout>
<livedIn>
<hasDepiction>
<likes>
<likes>
<likes>
What has a Creative Commons License?
Bob
Eric

Chicago, Illinois
On the shores
of Lake
Michigan,
Chicago is one
of the major…
<hasLicense>
<isAbout>
<livedIn>
<hasDepiction>
<likes>
<likes>
<likes>
What license does THIS document have?
Bob
Eric

Chicago, Illinois
On the shores
of Lake
Michigan,
Chicago is one
of the major…
<hasLicense>
<isAbout>
<livedIn>
<hasDepiction>
<likes>
<likes>
<likes>
What is liked by anyone who has lived somewhere
that is the subject of a document Bob has written?
Bob
Eric

SPARQL Queries

SPARQL Example #1
(specific endpoint – dbPedia)
Artists/Albums produced by Pharrell
PREFIX d: <http://dbpedia.org/ontology/>
SELECT ?artistName ?albumName
WHERE {
?album d:producer :Pharrell_Williams .
?album d:musicalArtist ?artist .
?album rdfs:label ?albumName .
?artist rdfs:label ?artistName .
FILTER ( lang(?artistName) = "en" )
FILTER (lang(?albumName) = "en" )
}

SPARQL Example #1

SPARQL Example #2
Musical artists who were born in
or have a hometown in Ireland
and the acts they performed with.

SPARQL Example #2
PREFIX dbo: <http://dbpedia.org/ontology/>
SELECT DISTINCT ?name ?person ?artist WHERE {
?person foaf:name ?name .
?person rdf:type <http://dbpedia.org/ontology/MusicalArtist> .
?person <http://dbpedia.org/ontology/associatedMusicalArtist>
?artist .
{
?person dbo:hometown
<http://dbpedia.org/resource/Republic_of_Ireland> .
}
UNION
{
?person dbo:birthPlace
<http://dbpedia.org/resource/Republic_of_Ireland> .
}
}
ORDER BY ?name

SPARQL Example #2

SPARQL Example #2
A major retailer ran this query…
associated it with the catalog of albums it sells…
and delivered a set of recommended purchases
for St. Patrick’s Day!

SPARQL Query #3
• Show me all landlocked countries
• With populations > 50,000
• Display the country names in English
• Eliminate duplicates

SPARQL Query #3
PREFIX type: <http://dbpedia.org/class/yago/>
PREFIX prop: <http://dbpedia.org/property/>
SELECT ?country_name ?population
WHERE {
?country a type:LandlockedCountries ;
rdfs:label ?country_name ;
prop:populationEstimate ?population .
FILTER (?population > 15000000 &&
langMatches(lang(?country_name), "EN")) .
} ORDER BY DESC(?population)

SPARQL Query #3 Results

SPARQL Query #3
PREFIX type: <http://dbpedia.org/class/yago/>
PREFIX prop: <http://dbpedia.org/property/>
SELECT ?country_name ?population
WHERE {
?country a type:LandlockedCountries ;
rdfs:label ?country_name ;
prop:populationEstimate ?population .
FILTER (?population > 15000000 &&
langMatches(lang(?country_name), "RU")) .
} ORDER BY DESC(?population)

• 8 KB text file with the .rdf extension
• Hosted on my website
• Information on me, my interests, and
people I know
My FOAF Profile

SPARQL Example #4
(generic endpoint)
FOAF (some people that Eric Franzon knows)
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name
FROM <http://ericaxel.com/eric.rdf>
WHERE {
?knower foaf:knows ?known .
?known foaf:name ?name .
}

SPARQL Example #4

Example #4 - Results

2 Disparate Data Sources:
2 FOAF Profiles

SPARQL Example #5
Querying two FOAF Profiles
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?name
FROM <http://ericaxel.com/eric.rdf>
FROM <http://bosatsu.net/foaf/brian.rdf>
WHERE {
?x rdf:type foaf:Person .
?x foaf:name ?name .
}

Where’s the Data?
What’s
The
Question?

Another Benefit of querying
Linked Data…
Data link to other data!
SPARQL Example #6

1. Find these pieces of information:
• Episode number
• Airdate
• Guest star
• Chalkboard gag
• Couch gag
2. Order them by Episode number
SPARQL Example #6

SPARQL Example #6
Bart Simpson's Linked Data (DBPedia)
SELECT ?epnum ?airdate ?guest_star ?chalkboard_gag
?couch_gag WHERE {
?s dbpedia2:airdate ?airdate .
?s dbpedia2:blackboard ?chalkboard_gag .
?s dbpedia2:guestStar ?guest_star .
?s dbpedia2:episodeNo ?epnum .
?s dbpedia2:couchGag ?couch_gag .
} order by ?epnum

SPARQL Example #6

Following the Trail…

One More Thing…

A little bit can be powerful!

Questions?
Operators are standing by.
THANK YOU!
eric@semanticfuse.com
@EricAxel
http://linkedin.com/in/ericfranzon
https://plus.google.com/+EricFranzon

• Semantic Markup Infusion
• Semantic Data Fusion
• Semantic SEO
• Semantic Roadmap/Audit
• Semantic Streamlining of Product Feeds/Catalogs
• Semantic Consulting/Training
• Semantic Interest Graph Generation

Resources
https://flic.kr/p/6krdsM
https://flic.kr/p/p9jiDK
https://flic.kr/p/3q8afL
https://flic.kr/p/brJs4G
https://flic.kr/p/78rsTc
https://flic.kr/p/bpSeR2
http://www.flickr.com/photos/dawnmanser/3532853278/
http://www.flickr.com/photos/artolog/3983764041/
http://www.flickr.com/photos/97964364@N00/59780745/
http://www.flickr.com/photos/starwarsblog/
http://aldobucchi.com
http://www.addletters.com/pictures/bart-simpson-generator/3024046.htm
http://richard.cyganiak.de/2007/10/lod/

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