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Tara Raafat

  1. 1. RDF and OWL the powerful Duo Presented by:Tara Raafat
  2. 2. • Why go semantic ? • Should I use RDF or OWL ? • What is the difference , what is the link ? • Did you say smart data ? Questions
  3. 3. Data Wisdom Knowledge Information • Taking decisions using knowledge and reasoning AI • Information used with experience and understanding  Semantic Web • Data enriched with context & history  ERP / CRM / MDM • TraditionalApplications SemanticWeb goes here Semantic web formalizes knowledge in a way that improves decisioning today, and can form the basis for autonomous reasoning in the future The SemanticWeb
  4. 4. SemanticWeb Layer Cake XML/XML Schema RDF RDFS Logic Proof URIUnicode Trust Query: SPARQL RulesOWL
  5. 5. RDF (Resource Description Framework)
  6. 6. RDF • Simple triple based data model Subject, Predicate, Object • Graph- based formalism for representing metadata • Kafka is the author of the book “Trial.” • Individual things, and not just files, are given unique identifiers. • XML serialization (RDF/XML) for ease of data exchange • Various textual representations for ease of human understanding • No Schema Kafka Trial Is Author of http://www.mybookstore.com/Trial http://www.mybookstore.com/isAuthorOf http://www.mybookstore.com/Kafka
  7. 7. RDF is the heart of Linked Open Data 7
  8. 8. AN RDF Integration Example
  9. 9. 9 English books database: Export data as RDF http://…isbn/000651409X Ghosh, Amitav http://www.amitavghosh.com The Glass Palace 2000 London Harper Collins a:name a:homepage a:author
  10. 10. 10 French books Data Base: Export data as RDF http://…isbn/000651409X Ghosh, Amitav Besse, Christianne Le palais des miroirs f:nom f:traducteur f:auteur http://…isbn/202038662 f:nom
  11. 11. 11 Merge your data http://…isbn/000651409X Ghosh, Amitav Besse, Christianne Le palais des miroirs f:nom f:traducteur f:auteur http://…isbn/2020386682 f:nom http://…isbn/000651409X Ghosh, Amitav http://www.amitavghosh.com The Glass Palace 2000 London Harper Collins a:name a:homepage a:author Same URI!
  12. 12. Ghosh, Amitav Besse, Christianne Le palais des miroirs f:original f:nom f:traducteur f:auteur http://…isbn/2020386682 f:nom Ghosh, Amitav http://www.amitavghosh.com The Glass Palace 2000 London Harper Collins a:name a:homepage a:author http://…isbn/000651409X a:author same as f:auteur
  13. 13. Merge with external data: Wikipedia Besse, Christianne Le palais des miroirs f:original f:nom f:traducteur f:auteur http://…isbn/2020386682 f:nom Ghosh, Amitav http://www.amitavghosh.com The Glass Palace 2000 London Harper Collins a:name a:homepage a:author http://…isbn/000651409X http://…foaf/Person r:type r:type http://dbpedia.org/../Amitav_Ghosh http://dbpedia.org/../The_Hungry_Tide http://dbpedia.org/../The_Calcutta_Chromosome http://dbpedia.org/../Kolkata http://dbpedia.org/../The_Glass_Palace r:type foaf:name w:reference w:author_of w:author_of w:author_of w:born_in w:isbn w:long w:lat
  14. 14. Ontologies & OWL ( Web Ontology Language)
  15. 15. 15 Ontology ( according to Tom Gruber (1992)) An ontology is a formal, explicit specification of a shared conceptualization Machine Readable Concepts, properties Relations, functions, Constraints, axioms, Are explicitly defined Consensual Knowledge Abstract model and simplified view of some phenomenon in the world that we want to represent
  16. 16. DifferentTeams: Different Languages … 11/17/2017 16 Business user with a data need Business data analyst Business -technology liaison Data architect Data scientist Data Consumers Design Dev – Data Source Application owner Developer Test Dev – Data Integration Developer Dev Ops Dev – Data consumer Code Dev Ops TestTest The problem that we are trying to solve Architectural concerns. Enterprise vision Semantics. Data dictionary Source system’s data terminology Source system’s data terminology. Target system’s terminology Target system’s data terminology The problem that we are trying to solve Functional solution Timing & priorities Dependencies
  17. 17. Ontology • a knowledge model which defines a set of concepts and the relationship between those concepts within a specific domain • Supports automated reasoning and inference of data using logical rules • Provides Knowledge sharing and reuse among people or software agents
  18. 18. OWL ( Web Ontology Language) • RDF based • A defacto standard for ontology development • Main components include Classes : which define concepts in a domain Properties: which are of two type Object properties : define relationships between concepts Datatype properties: define relationships between a concept and a literal Individuals: instances of classes Restrictions: Allow definition of cardinality restrictions as well existential and universal quantifications • Has three levels OWL Full OWL DL OWl-Lite
  19. 19. Project Example
  20. 20. Person Important Project Thing Project Research Project Development Project Partner Consulting Partner Employee Project Manager Project Coordinator Industrial Partner hasProjectManager hasProjectCoordinator • hasBudget • hasStartDate • hasEndDate • hasNumberOfEmployees hasPartner hasProjectCoordinator min 1 hasProjectManager exactly 1 hasBudget >=2,000,000 End date –Today <=1 month Biology Department Cancer Research Institute
  21. 21. Person Important Project Thing Project Research Project Development Project Partner Consulting Partner Employee Project Manager Project Coordinator Industrial Partner hasProjectManager hasProjectCoordinator • hasBudget • hasStartDate • hasEndDate • hasNumberOfEmployees hasPartner hasProjectCoordinator min 1 hasProjectManager exactly 1 hasBudget >=2,000,000 End date –Today <=1 month Biology Department Cancer Research Institute • hasBudget: 3,000,000 • hasStartDate: 01/08/2014 • hasEndDate: 01/12/2014 • hasNumberOfEmployees: 40 CR Proj CR Proj Sam Cohen Sarah SmithJohn Stevens John Stevens Cancer Research Institutes Project can only have one project managerSame person is manager and coordinator
  22. 22. So what happens when you combine OWL + RDF? SMART DATA Mapping Rules OWL Models RDF Data
  23. 23. Use of Ontologies • Knowledge representation • Semantic annotation • Semantic search • Back bone of Process Automation • Knowledge inference
  24. 24. An Industrial UseCase for SMART DATA
  25. 25. Legal Contract Legal PersonLegal Document Legal Person Legal Entity Item Invoice Customer Bill of Lading Consignee/Shipperhas invoice Letter of Credit Freight Forwarder Company Item buyer Trade Transaction Letter of Credit Bank Issuing Bank Account holds Vessel Vessel ID Journey Trade Profile vessel profile CIS systemsTransaction Data Trade Documents Marine traffic Website Port Date Price Has Price Total Price Port of Loading/ Port of Discharge Posted In Business Busines s Sector Smart Data forTrade Based Anti Money Laundering
  26. 26. Legal Contract Legal EntityLegal Document Legal Entity Company Item Cotton shirts Invoice #JS54 Customer ABCCorp Bill of Lading BOLLOC453 Consignee/Shipper has invoice Letter of Credit ABCCorpLOC453 Freight Forwarder Company ShippersStarItem buyer Trade Transaction #76873 Letter of Credit Bank BankUSA Issuing Bank Account #89222 holds Vessel BSLE9 Vessel ID V245 Journey Route548 Trade Profile JSmithProfile45 vessel profile CIS systemsTransaction Data Trade Documents Marine traffic Website Port Bangladesh-Jaipur- Amsterdam Date 7/08/2016 Price 2$ Has Price Total Price Port of Loading/ Port of Discharge Posted In Port Bangladesh- Amsterdam Price 54$ Total Number 27 Business NAICSCode767 Busines s Sector
  27. 27. Brief Answers • Why go semantic ? • To present knowledge about your data. • To allow data integration • To bring intelligence to your system • Should I use RDF or OWL ? • If you just want to link your data or annotate-> USE RDF • If you want to make your data smart and apply reasoning and inference -> USE OWL +RDF • What is the difference , what is the link ? • RDF is to present data in triple formats and give it some structure and unique identifiers so that data can be easily linked • OWL provides a rich vocabulary to add semantics and context and allow reasoning and inference • Did you say smart data ? • Yes! A data that can be understood by the computer and therefore allows for intelligent automation
  28. 28. THANK YOU Tara Raafat, PhD Email:tara.raafat01@mphasis.com

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