Structured Data for the Financial Industry

Ranking | Analytics | Search | Reporting
Structured Data for the Financial Industry
The extensions to schema.org
and their benefits for:
Trusted Open Data Ecosystem, September 28, 2017, Madrid, Spain
Dr. Mirek Sopek, Dr. Robert Trypuz

2TRUSTED OPEN DATA ECOSYSTEMS, MADRID, SPAIN, September 28, 2017
THE
WORKSHOP
AGENDA
• A Quest for Meaning
• On the open web
• In the business world
• The principle of least power
• The rise of schema.org
• Intro to schema.org
• Under the hood of schema.org
• Extending schema.org
• Applications of schema.org
• Rank
• Analytics
• Search
PART I – THE PRESENT

THE
WORKSHOP
AGENDA
• The reporting horizon
• The goal – to test the idea of the further simplification of
the reporting
• The relevant development:
• Semantics for XBRL
• The movement from within
• What we have done so far?
• MakoLab POCs & exercises
• A vision for the future steps
• Discussion
PART II – THE FUTURE

The PresentSCHEMA.ORG and its existing applications

INTRO
A QUEST
FOR MEANING
ON THE WEB
• The Web is (mostly) a Mess
• Metadata becomes (very often) Meta-Crap (after: Cory
Doctorow*)
• There is no such thing as Esperanto of the Web
(despite its importance, English is not a lingua franca)
• The trust is lost – people of the Web (often) live in echo-
chambers
THE WEB WAS IN THE DEEP NEED OF
A PRAGMATIC APPROACH
SHORTLY AFTER THE WEB WAS INVENTED
WE NOTICED THAT:
* https://www.well.com/~doctorow/metacrap.htm

INTRO
WEB
FULL OF MEANING
INVENTION
• The “Web Full of Meaning” was invented
(a.k.a. the “Semantic Web” or Web 3.0)
• Web gurus borrowed a fundamental term from philosophy –
ONTOLOGY - to name their Vocabularies.
• Using Ontologies (aka Vocabularies) they started to create
and promote new models for Data (Linked Data, Graph Data,
Smart Data)
TO COUNTERBALANCE THE MESS …

INTRO
DISSATISFACTION
• Most of the results were (so far) only good for academic research
• Almost none of our ontologies enjoyed wide adoption
• Promises to build Web 3.0 quickly turned out to be failed
THE WEB WAS IN THE DEEP NEED OF
A PRAGMATIC APPROACH
HOWEVER…

MEANWHILE
IN THE BUSINESS
WORLD …
• ISO20022, FpML, FIX, MISMO, XBRL, DPM, RIXML, IFX, OFX,
BPM6, SDMX, SDDS, MDDL, ACORD
• FIBO, ACTUS, DPM2ISO, SMCube
MULTIPLICITY of STANDARDS and PROJECTS
From:
Michał Piechocki:
„Trusted Open
Data Ecosystems”
Data Amplified 2016

SIR
TIM BERNERS LEE
• Principle: Powerful languages inhibit information reuse.
• Good Practice: Use the least powerful language suitable for
expressing information, constraints or programs on the World
Wide Web.
• Tradeoff: Choosing between languages that can solve a broad
range of problems and languages in which programs and data are
easily analyzed
PRINCIPLE OF LEAST POWER - 1998

…EARLY ATTEMPTS TO ACT WITH LESS POWER ..
• MCF (Meta Content Framework) – R. Guha 1995-1997
https://en.wikipedia.org/wiki/Meta_Content_Framework
• SHOE - Simple HTML Ontology Extensions – Sean Luke, Lee Spector, James Hendler, Jeff Heflin, and
David Rager, 1996
https://en.wikipedia.org/wiki/Simple_HTML_Ontology_Extensions
• RSS - RDF Site Summary – Dan Libby and Ramanathan V. Guha, 1999
• MICROFORMATS (μF) – a grassroots movement, 2005
https://en.wikipedia.org/wiki/Microformat
NONE OF THEM RECEIVED WIDESPREAD ADOPTION !!!!

…SO THE SCHEMA.ORG WAS INVENTED !
• Schema.org (2011), sponsored by the most important search engines: Google, Microsoft, Yahoo
and Yandex, is a large scale collaborative activity with a mission to create, maintain, and promote
schemas for structured data on the WEB pages and beyond.
• It contains more than 2000 terms: 753 types, 1207 properties and 220 enumerations.
• Schema.org covers entities, relationships between entities and actions.
• Today, about 15 million sites use schema.org. Random yet representative crawls (Web Data
Commons) show that about 30% of URLs on the web return some form of triples from schema.org.
• Many applications from Google (Knowledge Graph), Microsoft (like Cortana), Pinterest, Yandex and
others already use schema.org to power rich experiences.
• Think of schema.org as a global Vocabulary for the web transcending domain and language
barriers.
• The principal authors of the schema.org conceptual framework are R. Guha, D. Brickley and P. Mika

http://bl.ocks.org/danbri/raw/1c121ea8bd2189cf411c/
WHAT IS SCHEMA.ORG?
http://schema.org

SCHEMA.ORG USE SIMPLICITY – AN ILLUSTRATION
http://finances.makolab.com/HTML/LoanStudents/LoanStudents.html

UNDER THE HOOD OF SCHEMA.ORG
• „The driving factor in the design of Schema.org
was to make it easy for webmasters to publish
their data. In general, the design decisions
place more of the burden on consumers of the
markup.”
R.V. GUHA, D. DAN BRICKLEY, S. MACBETH –
„Schema.org - Evolution of Structured Data on the Web”
DESIGN DECISIONS
• Derived from RDFS (RDF Schema)
• Multiple inheritance hierarchy
• POLYMORPHIC PROPERTIES - Each property
may have one or more types as its domain
and its range („domainincludes” and
„rangeincludes”)
DATA MODEL

USAGE MODELS
• Under full control of site/messages/data
publishers
• Data EMBEDDED into page, data
representation or into message markup (HTML,
XML)
• Harvested during standard crawling, message
or data processing
SERIALIZATIONS
• RDFa - CANONICAL
• Microdata (native to HTML5)
• JSON-LD

CORE http://schema.org/<term> http://schema.org/<term>
HOSTED EXT. http://<ext>.schema.org/<term> http://schema.org/<term>
External EXT. http://<ext.domain>/<term> http://<ext.domain>/<term>
CORE http://schema.org/Car http://schema.org/Car
HOSTED EXT. http://auto.schema.org/Motorcycle http://schema.org/Motorcycle
External EXT. http://fibo.org/voc/BusinessEntity http://fibo.org/voc/BusinessEntity
EXTENSION MECHANISM: RULES FOR URIs
Documentation URI: Canonical URI:
Examples:
Rules:

<div itemscope itemtype="http://schema.org/BankTransfer">
<h1>If you want to donate</h1>
Send <span itemprop="amount" itemscope itemtype="http://schema.org/MonetaryAmount">
<span itemprop="amount">30</span>
<span itemprop="currency" content="USD">$</span>
</span>
via bank transfer to the
<span itemprop="beneficiaryBank">European ExampleBank, London</span>
Put "<i itemprop="name">Donate wikimedia.org</i>" in the transfer title.
</div>
EXAMPLES - MICRODATA

<div vocab="http://schema.org" typeof="BankTransfer">
<h1>If you want to donate</h1>
Send <span property="amount" typeof="MonetaryAmount">
<span property="amount">30</span>
<span property="currency" content="USD">$</span>
</span>
via bank transfer to the
<span property="beneficiaryBank"> European ExampleBank,London</span>
Put "<i property=’name’>Donate wikimedia.org</i>" in the transfer title.
</div>
EXAMPLES - RDFa

SCHEMA.ORG VS. ONTOLOGIES AND LINKED DATA
• Common elements: a graph data model of
typed entities with named properties
• Schema.org uses RDFS schema language and
JSON-LD and RDFa syntaxes
• Schema.org shares (with Linked Data and
Ontologies) many of the same goals
• Linked data and ontologies have brought to the
Web a much smaller number of data sources
than Schema.org, but their quality is (often) very
high. This opens up many opportunities for
combining the two approaches—for example,
professionally published ontologies can often
authoritatively describe the entities mentioned in
Schema.org descriptions from the wider
mainstream Web.
SIMILARITIES DIFFERENCES
• Schema.org's approach can be seen as less noisy and decentralized
than Linked Data
• Schema.org promotes syntaxes (microdata, RDFa) that are a tradeoff
between machine-friendly and human-friendly formats
• Linked RDF data publication practices have not been adopted in the
Web at large
• Schema.org shares the Linked-Data community's skepticism toward
the premature ontologies (rule systems, description logics, etc.) found
in much of the academic work that is carried out under the Semantic
Web banner.
• Schema.org avoids assuming that rule-based processing will be
commonplace
• Schema.org’s approach, in contrast to the methodologies of building
Linked Data and ontologies, does not assume that various kinds of
cleanup, reconciliation, and post-processing will usually be needed
before structured data from the Web can be exploited in applications.
• Many frame-based knowledge representation systems, including RDF
Schema and OWL have a single domain and range for each relation.
Schema.org assumes polymorphism.
• Schema.org allows for multiple inheritance.

CORE  HOSTED EXTENSIONS  EXTERNAL EXTENSIONS
• CORE – „Core, basic vocabulary for describing the kind of entities the most
common web applications need”*
• HOSTED/REVIEWED EXTENSIONS – Domain specific basic vocabularies.
• EXTERNAL EXTENSIONS – More specialized, fully independent domain
specific vocabularies. Built by a third party.
• Today: autos, finance, bibliography, health & life-sciences, iot
EXTENSION MECHANISM: SEQUENCE OF SPECIFICITY
* http://schema.org/docs/extension.html

CREATING EXTENSIONS TO SCHEMA.ORG
• Extension URI: auto.schema.org
• Designed as the first phase of the GAO project
(Generic Automotive Ontology -
http://automotive-ontology.org)
• First step: extending core vocabulary by a
minimal set of new terms (May 2015)
• Second step: creating auto.schema.org hosted
extension (May 2016)
• Third step: creating POC of the external
extension (March 2017)
• Extension URI: fibo.schema.org
• Inspiration from FIBO project (Financial
Industry Business Ontology – http://fibo.org )
• Going through BOC (Bag-Of-Concept) phase
and using an „Occam Razor” approach.
• First step: extending core vocabulary by a
minimal set of new terms (May 2016)
• Second step: creating fibo.schema.org hosted
extension (published in pending.schema.org
(March 2017))
• Third step: creating POC of the external
extension (March 2017)
AUTOMOTIVE EXTENSION FINANCIAL EXTENSION

AUTO.SCHEMA.ORG
May 13, 2015
– official introduction
of the Automotive extension
to schema.org
Collaborative project
of Hepp Research GmbH, MakoLab SA
and many other individuals.

FIBO.SCHEMA.ORG
Extension of the core vocabulary
by a minimal set of new terms
(May 2016)
The hosted extension
(published March 2017) as
pending.schema.org
Collaborative project
of an international group of individuals lead by
MakoLab SA.
Described in:
http://schema.org/docs/financial.html

The financial extension of schema.org refers to
the most important real world objects related to
banks and financial institutions:
• A bank and its identification mechanism
• A financial product
• An offer to the client
• Described in:
Thing CLASSES
Action
TransferAction
MoneyTransfer
Intangible
Service
FinancialProduct
BankAccount
DepositAccount
CurrencyConversionService
InvestmentOrDeposit
BrokerageAccount
DepositAccount
InvestmentFund
LoanOrCredit
CreditCard
MortgageLoan
PaymentCard +
PaymentService
StructuredValue
ExchangeRateSpecification
MonetaryAmount
RepaymentSpecification
FIBO.SCHEMA.ORG

FIBO.SCHEMA.ORG
The financial extension of schema.org refers to
the most important real world objects related to
banks and financial institutions:
• A bank and its identification mechanism
• A financial product
• An offer to the client
• Described in:

A BANK
A DEPOSIT ACCOUNT
A PAYMENT CARD
THE BASIC MODELS OF
THE FINANCIAL OBJECTS
FIBO.SCHEMA.ORG

CREATING EXTENSIONS – THE ART OF HARD
CHOICES
• All additions to schema.org, to its core and to a „hosted” extension must meet extremely strict
conditions:
• Their number must be minimal compared to the size of the vocabulary of the domain the extension
represents. The making of an extension is an endless trade-off between the need for the expressive
vocabulary of the domain and the requirement for its minimalism.
• They must represent the CUSTOMER NEEDS and adopt down „bottom-up” design rules – not the
demands of the domain specialists and practitioners.
• The bottom-up approach assumes the „BOC” (Bag Of Concepts) approach, where the elements of the
bag stem from the public „discourse” (the search on the web, social media)
• The extensions must reuse the existing schema.org terms wherever possible, even if the current meaning
of them may differ from the expected meaning.
Why is the creation of schema.org extension the Art of Hard Choices?

The Applications. I.
RankWEB SEARCH REDEFINED

FUNDAMENTAL TRENDS IN WEB SEARCH
1. BIGGER
SHARE ON THE
TRANSACTION
2. RICHER
INTERACTION
This slide is based on the work of M. Hepp & M. Sopek "Web Search and Beyond: Digital Marketing for Automotive"

4. DYNAMICS
AND VOLATILITY
3. STRONGER
INDIVIDUALIZATION
FUNDAMENTAL TRENDS IN WEB SEARCH

RICH SNIPPETS KNOWLEDGE PANEL
VISUAL FEATURES IN SEARCH ENGINES

FACTUAL ANSWERS
And more …
TABULAR RESULTS
VISUAL FEATURES IN SEARCH ENGINES

CONCRETE BENEFITS
Rich snippet results on 2nd
position received higher CTR
than standard snippet on 1st
position
CTR INCREASE EXAMPLE

What you measure in a traditional way
may not reflect
your actual performance
Solutions:
• Use KPIs with care
• New metrics based on external resources
• Add granular event handlers
MEASURE WITH CARE
NEW METRICS NEEDED

SUMMARY OF “RANK” BENEFITS OF SCHEMA.ORG
• CTR increase (Rich Snippets effect)
• Better Brand visibility
(Knowledge Panels and Factual Answers)
• Better Product positioning
(Rich snippets & Tabular results)
• Faster way to reach searched content
(more sitelinks)
• Better mobile device experience of
search
11.09.2015 – Google:
„Over time, I think it [structured markup] is
something that might go into the rankings as well.
If we can recognize someone is looking for a car, we can
say oh well, we have these pages that are marked up
with structured data for a car, so probably they are
pretty useful in that regard. We don’t have to guess if
this page is about a car.”
John Mueller / Webmaster Trends Analyst @Google

WHAT ELSE CAN WE DO WITH SCHEMA?
• While schema.org was invented to help search engines in their job and to help site owners to be
more reliably discovered and ranked on the Search Engine Results Pages – its benefits are much
more profound.
• This why we say that schema.org power goes beyond RANK, and allows you to ANALYZE your site
market environment better, improve site convergence and LEADS generation and helps to deliver a
new kind of SEARCH capacity for your site!
• What is more, to SEARCH and to ANALYZE you don’t need Google to cooperate 

The Applications. II.
AnalyseNEW KIND OF DATA ANALYTICS

SCHEMA.ORG DATA IN GOOGLE ANALYTICS
The markup
in the website’s code
• Schema.org
Google
Tag Manager
• Additional
setup
Google
Analytics
• Additional
Dimensions
and Metrics

Auto
Model 1
- Name
- Brand
Version1
Model, fuelConsumption,
fuelType,
numberOfDoors, Color
Version 2
Version 3
Model 2
- Name
- Brand
Version 1
Version 2
Version 3
Model 3
- Name
- brand
Version 1
Version 2
Version 3
POC 1

http://wisem.makolab.pl/ga/model1.html

Usage within GA

Usage within GA
Which colour of a car
should be used
in Display Campaigns
or in TV ads for Car1?

Which engine model of Car1 is most popular online?
Should we spend campaign money on Sport version or on Eco version?
Usage within GA

FINANCIAL EXTENSION SCHEMA.ORG POC
• http://finances.makolab.com
• Full use of fibo.schema.org
• Definitions of financial dimensions
• Analytics with Google “GA”
POC 2

POC’s page Json property Dimension Dimension name
BankAccount.html price Bank Account Fee Price
name Financial Product
Name
Financial Product
Name
BrokerageAccount.ht
ml
minValue Brokerage Account
Minimum Investment
Minimum
Name
Financial Product
Name
CreditCard.html annualPercentageRate Credit Card APR Percentage Rate
minValue Credit Card Required
Collateral
Minimum
price Credit Card Annual Fee Price
Name
Financial Product
Name
CreditCard8.html name Financial Product
Name
Financial Product
Name
minValue Credit Card Limit Minimum
PaymentService.html name Financial Product
Name
Financial Product
Name
FinancialProducts.html name Financial Product
Name
Financial Product
Name
minValue Minimum Insurence
Coverage
Minimum
maxValue Maximum Insurence
Coverage
Maximum
FINANCIAL EXTENSION SCHEMA.ORG POC

TRUE DATA ANALYTICS

PROS: CONS:
• None.• Analyse additional information available in
Schema markup right in Web Analytics.
• Better insights into what people look at on
the website. Deeper understanding of users’
needs.
• Better conclusions for website’s UX
optimization.
• Better conclusions for campaigns
optimization.

The Applications. III.
SearchADD SMART SEARCH TO YOUR SITES

INTELLIGENT/SMART SEARCH BASED ON
SCHEMA.ORG MARKUP
Mark your product data
with schema.org markup
Run the smart Search Crawler
for an Enterprise Website
Check for schema.org
markup (Microdata or JSON-LD)
When markup is found, create
property map and assign values
Display enhanced search results

Corporate product page + microdata
http://nusil.com/product/r-2370_rtv-silicone-rubber-foam
SCHEMA.ORG MARKUP

UNDER THE HOOD…
Crawler
Indexer
(Lucene)
Microdata
found
Semantic
Data
WebSite
SCHEMA.ORG MARKUP

SEARCH AGAINST BOTH CONCEPTS AND THEIR PROPERTIES’ VALUES
The real values taken from existing data found
by crawler within the marked website pages
SCHEMA.ORG MARKUP

SEARCH AGAINST MULTIPLE CRITERIA
SCHEMA.ORG MARKUP

Practical Session
❧
How to use it?

USING SCHEMA.ORG IS EASY !
POC for financial domain: IMPLEMENTATION STEPS:
• Understand the domain your website
belongs to
• Find schema.org types and properties
that can be used to mark up your data
• Add markup to your web pages – use
types and properties properly!
• As a general rule, you should mark up
only the content that is visible to
people who visit the web page
• The more content you mark up, the
better
• Test your markup (use: Google’s rich
snippets testing tool)
http://finances.makolab.com

Reporting
PERSPECTIVES FOR BUSSINESS REPORTING
SCHEMA.ORG EXTENSIONS
The Future
❧

THE REPORTING HORIZON
THE BUSINESS REPORTING and BUSINESS INFORMATION EXCHANGE IS
REIGNED BY XBRL STANDARD
• However, the cost of filing financial reports is still quite high, particularly for small companies*
• This is why in the US, “Small Company Disclosure Simplification Act” :
“(…) exempts emerging growth companies and issuers with total annual gross revenues of less than
$250 million from the requirement to use Extensible Business Reporting Language (XBRL) for financial
statements and other mandatory periodic reporting filed with the Securities and Exchange
Commission (SEC). Such companies, however, may elect to use XBRL for such reporting.”
* $2,000 to $25,000 per year according to XBRL US.

THE PURPOSE OF THIS PART OF THE WORKSHOP
TO DISCUSS THE POSSIBILITY OF DEEPER SIMPLIFICATION OF XBRL by
adoption of schema.org principles
• We have performed a series of simple technical exercises that pave the initial path for further
studies
• While we do not propose here any new standard nor want to shake the foundations of the old,
we think it is worth to consider if schema.org principles offer the possibilities to make business
reporting even simpler and more accessible.

THE RELEVANT DEVELOPMENT
THE USE OF SEMANTIC WEB STANDARDS
• „Publishing XBRL as Linked Open Data”
(Roberto García & Rosa Gil, Universitat de Lleida)
• „Triplificating and linking XBRL financial data”
(Roberto García & Rosa Gil, Universitat de Lleida)
• „Adopting Semantic Technologies for Effective
Corporate Transparency”
(Maria Mora-Rodriguez, Ghislain Auguste Atemezing,
Chris Preist)
• „Financial Report Ontology”
(Charles Hoffman)

THE RELEVANT DEVELOPMENT
THE EVOLUTION WITHIN XBRL WORLD
• „Open Information Model” -
https://specifications.xbrl.org/work-product-
index-open-information-model-open-
information-model.html The Open Information Model
provides a syntax-independent model for XBRL data, allowing reliable
transformation of XBRL data into other representations. The work
product includes: xBRL-XML, xBRL-JSON, xBRL-CSV, OIM Common.
• XBRLS - XBRL Simple Application Profile
(how a simpler XBRL can make a better XBRL)
• Inline XBRL - https://specifications.xbrl.org/spec-
group-index-inline-xbrl.html

HOW COULD IT WORK?
POC: FIBO as schema.org
external extension
• The extension URI: http://fibo.org/voc/
• The conversion from FIBO-V
(SKOS complaint ontology)
• The markup example based
on the extension

* Charles Hoffman:
http://xbrl.squarespace.com/journal/2008/12/18/
hello-world-xbrl-example.html
HOW COULD IT WORK?
INITIAL EXCERSISE “I”- XBRL
„Hello World” * expressed as schema.org
compliant markup
• Converting taxonomy (XSD) to OWL ontology
(with help of: http://rhizomik.net/html/redefer/)
• Writing schema.org compliant JSON-LD markup

HOW COULD IT WORK?
INITIAL EXCERSISE “II”- iXBRL
example
• Based on https://www.xbrl.org/ixbrl-
samples/valeo-income-statement.html
• Expression of the data semantics in JSON-LD –
schema.org compliant markup

HOW COULD IT WORK?
INITIAL EXCERSISE “III”- GAAP
TAXONOMY IN SCHEMA.ORG FORMAT
• Source: PROPOSED 2018 US GAAP FINANCIAL
REPORTING TAXONOMY
• How: Extracting parent-child taxonomy with
the definitions of terms + schema.org-like RDFa
formatting of the obtained model
• Result: http://sdo-gaap-
ee.appspot.com/GrossProfit

A VISION for the future steps
CREATION of SCHEMA.ORG extensions and their applications
• Step I – the external extension based on selected XBRL taxonomy (like GAAP or IFRS)
• Step II – the external extension based on selected SBR taxonomy
• Creation of implementation guidelines and live POC
• Working with interested parties on the real-life tests
• Critical evaluation of the project
• If successful - working on the HOSTED EXTENSION to schema.org
• In general - Adopting the philosophy of bottom-up, empirical approach to the creation

Discussion
Let’s evaluate the soundness of the ideas presented here …

THANK YOU
Full slide deck at: http://ml.ms/makolabtode
❧

PLEASE CONTACT US!
DR. MIREK SOPEK
CTO
sopek@makolab.com
Poland:
MakoLab SA,
Demokratyczna 46,
93430 Lodz, Poland
Phone: +48 600 814 537,
www.makolab.com
USA:
Makolab USA Inc,
20 West University Ave,
Gainesville, FL 32601
Phone: +1 551 226 5488 ,
www.makolab.com
Dr ROBERT TRYPUZ
MakoLab SA
Rzgowska 30
93-172 Łódź
Poland
robert.trypuz@makolab.com
INDUSTRY
MakoLab SA
Rzgowska 30
93-172 Łódź
Poland
robert.trypuz@makolab.com
ACADEMIA
JPII University
Lublin
trypuz@kul.pl

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