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Industry Ontologies: Case Studies in Creating and Extending


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Industry Ontologies: Case Studies in Creating and Extending for Industry-specific Vocabularies

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Industry Ontologies: Case Studies in Creating and Extending

  1. 1. Dr Mirek Sopek Dr Robert Trypuz, Dominik Kuziński MakoLab SA
  2. 2. • It is impossible to forget that Semantic Web and „Semantics” as we use it in our track title, owes much to Sir Tim Berners Lee – the inventor of Web and the Semantic Web • Tim received yesterday ACM Turing Award, nicked named: Nobel of Computing
  3. 3. • (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. • covers entities, relationships between entities and actions. • Today, about 15 million sites use Random yet representative crawls (Web Data Commons) show that about 30% of URLs on the web return some form of triples from • Many applications from Google (Knowledge Graph), Microsoft (like Cortana), Pinterest, Yandex and others already use to power rich experiences. • Think of as a global Vocabulary for the web transcending domain and language barriers.
  4. 4.
  5. 5. • The Industry Ontologies are the subclass of the DOMAIN ONTOLOGIES. • They are created to represent concepts that are used in a given industry • They define valid meanings of concepts that are used in the industry • The essential character of the Industry Ontologies is pragmatism – they must be useful, practical and easy to use. • Some examples of the Industrial Ontologies: FIBO (Finance), GoodRelations (e-commerce), VVO (Volkswagen Vehicle Ontology), UCO (Used Cars Ontology), GSPAS Ontology (Ford Ontology for Global Study Process Allocation System), POPE (Purdue Ontology for Pharmaceutical Engineering) …
  6. 6. Case studies: • Automotive ontology – “core”  – “hosted extension”  PURL.ORG/GAO – “external extension” • Financial ontology – “core”  – “hosted extension”  - „external extension”
  7. 7. Design Decisions • „The driving factor in the design of 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 – „ - Evolution of Structured Data on the Web” Data Model • 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”)
  8. 8. 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
  9. 9. Extension mechanism: sequence of specificity CORE  HOSTED EXTENSIONS  EXTERNAL EXTENSIONS CORE – „Core, basic vocabulary for describing the kind of entities the most common web applications need”* (Built by team, extended by proposals from community, managed by a community process with the leading role of steering committee.) HOSTED/REVIEWED EXTENSIONS – Domain specific basic vocabularies. The hosted extensions are reviewed, versioned and published as part of itself to ensure consistency with the core and its flat namespace. (Built by the specific interest groups respecting the community process, reviewed by the community and approved by the steering committee). EXTERNAL EXTENSIONS – More specialized, fully independent domain specific vocabularies. Built by a third party. May go through a feedback process, yet they are hosted and controlled by the third party to serve its specific application needs. *
  10. 10. Extension mechanism: rules for URIs CORE<term><term> HOSTED EXT. http://<ext><term><term> External EXT. http://<ext.domain>/<term> http://<ext.domain>/<term> Documentation URI: Canonical URI: CORE HOSTED EXT. External EXT. Examples: Rules:
  11. 11. Examples - MICRODATA div itemscope itemtype=""> <h1>If you want to donate</h1> Send <span itemprop="amount" itemscope itemtype=""> <span itemprop="amount">30</span> <span itemprop="currency" content="USD">$</span> </span> via bank transfer to the <span itemprop="beneficiaryBank">European ExampleBank, London</span> Put "<i itemprop="name">Donate</i>" in the transfer title. </div>
  12. 12. Examples - RDFa <div vocab="" typeof="BankTransfer"> <h1>If you want to donate</h1> Send <span property="amount" typeof="MonetaryAmount"> <span property="amount">30</span> <span property="currency" content="USD">$</span> </span> via bank transfer to the <span property="beneficiaryBank">European ExampleBank,London</span> Put "<i property=’name’>Donate</i>" in the transfer title. </div>
  13. 13. Examples – JSON-LD <script type="application/ld+json"> {"@context": "", "@type": "BankTransfer", "name": "Donate", "amount": { "@type": "MonetaryAmount", "amount": "30", "currency": "USD" }, "beneficiaryBank": "European ExampleBank, London" } </script>
  14. 14. Automotive Extension • Extension URI: • Designed as the first phase of the GAO project (Generic Automotive Ontology - • First step: extending core vocabulary by a minimal set of new terms (May 2015) • Second step: creating hosted extension (May 2016) • Third step: creating POC of the external extension (March 2017) Financial extension • Extension URI: • Inspiration from FIBO project (Financial Industry Business Ontology – ) • 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 hosted extension (published in (March 2017)) • Third step: creating POC of the external extension (March 2017)
  15. 15. May 13, 2015 – official introduction of the Automotive extension to Collaborative project of Hepp Research GmbH, MakoLab SA and many other individuals.
  16. 16. … can now be brought to the Web with the extension: See: for more information
  17. 17. • Extension URI: • Based on GAO project (Generic Automotive Ontology) ontology • More than 300 classes and 40 properties • Used to drive SMART search for an automotive client • See:
  18. 18. Extension of the core vocabulary by a minimal set of new terms (May 2016) The hosted extension (published March 2017) as Collaborative project of an international group of individuals lead by MakoLab SA. Described in:
  19. 19. The financial extension of 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
  20. 20. The financial extension of 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 PROPERTIES Property annualPercentageRate feesAndCommissionsSpecification interestRate identifier leiCode duration loanTerm requiredCollateral accountMinimumInflow accountOverdraftLimit amount bankAccountType beneficiaryBank cashBack contactlessPayment currency currentExchangeRate domiciledMortgage downPayment earlyPrepaymentPenalty exchangeRate exchangeRateSpread floorLimit gracePeriod loanMortgageMandateAmount loanPaymentAmount loanPaymentFrequency loanRepaymentForm loanType monthlyMinimumRepaymentAmount numberOfLoanPayments recourseLoan renegotiableLoan
  21. 21. A bank Deposit Account Payment card The basic models of the financial objects
  22. 22. • Extension URI: • Based on FIBO project (Financial Industry Business Ontology) ontology – Business Entities • Used in the POC for SEO, analytics and search.
  23. 23. • Flat namespace (moderate requirement) • views (showing super- and sub- types for a given type, showing properties that can be used) • References to for common types and properties • URI stability and persistence • Good taxonomy • Good and comprehensive labels • Not many restrictions, e.g. property polymorphism not required Many ontologies can qualify for the transformation !!!
  24. 24. The Web Structured Data Revolution Knowledge Graphs, Rich Snippets, Conversational Search, Info Boxes, Knowledge Panels, Semantic Search, Answer Boxes, RankBrain, Semantic SEO, Rich Cards, Enhanced Analytics and more …
  25. 25. I. DATA analytics for Websites using II. Intelligent/Smart search based on markup III. Enterprise taxonomies & vocabularies • Work for both Intra-, Extra- and Inter-net portals • Does not need Google to cooperate  • Not limited to „core” or „hosted extensions” • Works with all serializations, but the easiest is JSON-LD. • Minimal skills required to create relevant markup
  26. 26. Markup in website’s code • or external extension Google Tag Manager* • Additional setup Google Analytics** • Additional Dimensions and Metrics How does it work? * Other Tag Managers possible ** Other analytics platforms possible
  27. 27. Proof-Of-Concept: 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
  28. 28.
  29. 29. • Mark your product data with markup • Run the smart Search Crawler for an Enterprise Website • Check for markup (Microdata or JSON-LD) • When markup found, create property map and assign values • Display enhanced search results
  30. 30. Corporate product page + microdata
  31. 31. Crawler Indexer (Lucene) Microdata found Semantic Data WebSite
  32. 32. The real values taken from existing data found by crawler within the marked website pages
  33. 33. • External extensions to are ideal for exposing enterprise taxonomies • OWL ontologies can be “projected” onto external format • No loss of ontology expressivity • The best example: “GS1 Web Vocabulary” • GAO, FIBO external extension POCs “A well-constructed enterprise taxonomy is central to multiple business functions, including Business Intelligence, Content Strategy a nd Management, Digital Asset Management, Knowledge Management, and User Experience.” Strategic Content (
  34. 34. • is an extensible framework to build (convert) industrial ontologies • Extremely easy to use • It’s principal use is to enable Structured Data Revolution • It can also be used for an enterprise’s own needs: • Enhancing enterprise data quality and meaning by delivering easy to use vocabulary/taxonomy solution • Enabling data analytics • Enabling smart search • External extensions to can be used to express most of the industrial ontologies (easy to match requirements) • Bridges the gap between enterprise data formats and public web data
  35. 35. Robert Trypuz MakoLab SA Rzgowska 30 93-172 Łódź Poland Dominik Kuziński MakoLab SA Rzgowska 30 93-172 Łódź Poland MakoLab USA Inc. 20 West University Ave., Gainesville, FL 32601 USA +1 551 226 5488 MakoLab SA Demokratyczna 46 93-430 Lodz Poland +48 600 814 537 Dr Mirek Sopek