My Presentation at SMXL Milan 2018 with David Amerland. Together we gave an overview on the strategies and principles of application of structured data and how it can improve discoverability and increase accuracy of Search for a much better user experience. But first you need to understand HOW structured data works and how you can implement on your digital assets

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6-7-8, NOVEMBER 2018
Advanced Structured Data
Sante J Achille
Search Marketing Specialist

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What I’ll be addressing today…
• Benefits of Structured Data
• A closer look at the underlying principles
• Practical implementation
• Examples
• Q&A – all you can eat!

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Benefits
Less Noise, More Conversions – Some Facts on Performance

4. Before SEO
After SEO
N° Queries in Google Search Console
After Structured Data
Implementation
&
Entity Identification

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Performance

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Performance

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Performance
NOTE:
Data does not include leads from
Google Local Business

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The Theory…
Understanding structured data

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“An information web is an organic entity that grows from the
interests and energy of the communities that support it”
“Anyone can say Anything about Any Topic”
Semantic Web for the Working Ontologist
Dean Allemang – Jim Hendler

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Organizing Data Across the Web
How is it done??

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The Resource Description Framework (RDF) defines
information about resources. Resources can be anything:
documents, people, physical objects or abstract concepts.
http://www.w3.org/TR/rdf11-primer/
• Information is represented by a Node-Arc model…

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In RDF the description of a resource is represented by a series of triples. The
components of each triple are the Subject - Predicate – Object
A triple emulates the structure of a simple phrase such as “Sante lives in L'Aquila…”

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The Subject of the triple is the URI, identifying the resource being described
The Object may be a value (string, number, data, …) or the URI of another resource which is
somehow related to the subject
The Predicate explains the relationship between subject and object and is a URI chosen
amongst those available in the various Vocabularies …

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Let’s see this in action!
A JSON-LD file and equivalent Triples

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Example: a simple phrase
Mario Rossi works for Azienda Srl. Their corporate VATID is
ITXXXXXXXXXXX, they can be reached via email at info@aziendasrl.it or
you can call at +39. XXX XX XX XXX

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The underlying concepts
Mario Rossi is a Person
He worksFor Azienda Srl
Azienda Srl has VatID ITXXXXXXXXXXX
Azienda Srl has email info@azienda.it
Azienda Srl telephone number is +39. XXX XX XX XXX

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Equivalent in RDF Triples
<https://www.aziendasrl.it/> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/Organization> .
<https://www.aziendasrl.it/> <http://schema.org/legalName> "Azienda Srl" .
<https://www.mariorossi.it/> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/Person> .
<https://www.mariorossi.it/> <http://schema.org/name> "Mario Rossi" .
<https://www.aziendasrl.it/> <http://schema.org/email> "mario.rossi@aziendasrl.it" .
<https://www.mariorossi.it/> <http://schema.org/worksFor> <https://www.aziendasrl.it/> .
<https://www.aziendasrl.it/> <http://schema.org/telephone> "+39. XXX XX XX XXX" .
<https://www.aziendasrl.it/> <http://schema.org/vatID> "ITXXXXXXXXXXX" .
http://rdf-translator.appspot.com/

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{
"@context" : "https://www.schema.org/",
"@id" : "https://www.mariorossi.it/",
"@type" : "Person",
"name" : "Mario Rossi",
"worksFor" :
{
"@type" : "Organization",
"@id" : "https://www.aziendasrl.it/",
"legalName" : "Azienda Srl",
"vatID" : "ITXXXXXXXXXXX",
"email" : ”info@aziendasrl.it",
"telephone" : "+39. XXX XX XX XXX"
}
}
The JSON-LD File
Subject
Predicate Object

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The JSON-LD File
Subject Predicate Object

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Implementing Structured Data
Content Modelling

21. Schema Markup for Documents
{
"@context" : "https://www.schema.org/",
"@id" : "https://www.mariorossi.it/documents/",
"@type" : "DigitalDocument",
"name" : "Approved Waivers",
"about" :
{
"@id" : "https://www.uscis.gov/i-601",
"@type" : Thing”,
"name" : "I-601, Application for Waiver of Grounds of Inadmissibility"
},
"hasPart" :
{
"@id" : "https://www.mariorossi.it/documents/waiver.png",
"@type" : "ImageObject",
"name" : "Form I-797, Notice of Action",
"description" : "application for travel document: approval notice. ",
"about" :
{
"@id" : "https://www.wikidata.org/wiki/Q5422397",
"@type" : "Thing",
"name" : "Extreme Hardship"
}
}
}

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Equivalent in RDF Triples
<https://www.mariorossi.it/documents/> <http://schema.org/hasPart> <https://www.mariorossi.it/documents/waiver.png> .
<https://www.mariorossi.it/documents/waiver.pdf> <http://schema.org/description> "application for travel document: approval notice. " .
<https://www.mariorossi.it/documents/waiver.pdf> <http://schema.org/name> "Form I-797, Notice of Action" .
<https://www.wikidata.org/wiki/Q5422397> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/Thing> .
<https://www.wikidata.org/wiki/Q5422397> <http://schema.org/name> "Extreme Hardship" .
<https://www.uscis.gov/i-601> <http://schema.org/name> "I-601, Application for Waiver of Grounds of Inadmissibility" .
<https://www.mariorossi.it/documents/> <http://schema.org/about> <https://www.uscis.gov/i-601> .
<https://www.mariorossi.it/documents/> <http://schema.org/name> "Approved Waivers" .
<https://www.mariorossi.it/documents/> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/DigitalDocument> .
<https://www.mariorossi.it/documents/waiver.pdf> <http://schema.org/about> <https://www.wikidata.org/wiki/Q5422397> .
<https://www.mariorossi.it/documents/waiver.pdf> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/ImageObject> .
http://rdf-translator.appspot.com/

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Disambiguation (Entity Identification)

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https://search.google.com/structured-data/testing-tool

27. https://search.google.com/structured-data/testing-tool

28. Entities

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Wikidata

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Wikidata
Wikidata is a collaboratively edited knowledge base, providing a
common source of data which can be used by Wikimedia projects such
as Wikipedia, and by anyone else, under a public domain license.

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What we have addressed today…
• Benefits of Structured Data
• A closer look at the underlying principles
• Practical implementation
• Examples

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Conclusions
• Understand your Content
• Create a complete model using schema.org
• Implement by means of a JSON-LD file
• Identify all relevant entities at page level
• Disambiguate (WikiData, dBPedia, …)
• Validate your model – Avoid Errors
• Avoid Structured data SPAM Penalties
• Remember content modelling is an art: just because it validates, it
doesn’t mean it’s perfect: Test, test and test again!

33. Thank You :)
Sante J. Achille
• Search Marketing Consultant
• Chairman SMXL.IT
• sante@achille.name
• https://achille.name/
• @sjachille
• Biography & CV
Contact me for more info on my next Structured Data Workshop!