Semantic Web for Enterprise Architecture

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  • Semantic Web for Enterprise Architecture

    1. 1. The Semantic Web for Enterprise Architecture James Lapalme
    2. 2. Me, Myself and I <ul><li>Working on semantics and modeling problems since 2001 (E-learning, SoC) </li></ul><ul><li>Enterprise architect at PSP Investments with a focus on Information </li></ul><ul><li>PhD candidate at UdeM (MPSoC) </li></ul><ul><li>IEEE/ACM author and presenter </li></ul>
    3. 3. Objectives <ul><li>Introduction to the Semantic Web </li></ul><ul><li>Application to Enterprise Architecture </li></ul><ul><li>Discussion on Possible Trends </li></ul>
    4. 4. Agenda <ul><li>Enterprise Goals and Challenges </li></ul><ul><li>The Semantic Web </li></ul><ul><li>Information Modeling </li></ul><ul><li>The Semantic Web in the Context of EA </li></ul><ul><li>Future Applications </li></ul>
    5. 5. Scary Words <ul><li>Semantics </li></ul><ul><li>Ontology </li></ul><ul><li>Meaning </li></ul><ul><li>Conceptualization </li></ul><ul><li>Model </li></ul><ul><li>Formal </li></ul><ul><li>Metadata </li></ul>
    6. 6. Enterprise Architecture Goals <ul><li>Process Adaptation </li></ul><ul><ul><li>Rapid Time-to-Market </li></ul></ul><ul><li>Process Optimization </li></ul><ul><ul><li>Lower operational costs </li></ul></ul><ul><li>Knowledge Discovery </li></ul><ul><ul><li>Higher Return </li></ul></ul><ul><li>Data Quality </li></ul><ul><ul><li>Lower Risk </li></ul></ul>
    7. 7. Information Challenges <ul><li>Ambiguous Semantics </li></ul><ul><ul><li>Communication </li></ul></ul><ul><li>Multiple Technologies </li></ul><ul><ul><li>Consistency </li></ul></ul><ul><li>Partially Known Value-Chain </li></ul><ul><ul><li>Operations </li></ul></ul><ul><li>Low Data Quality </li></ul><ul><ul><li>Decisions </li></ul></ul><ul><li>Poor Data Specification </li></ul><ul><ul><li>Expectations </li></ul></ul>
    8. 8. Modern Solutions <ul><li>SOA </li></ul><ul><ul><li>Process Adaptation </li></ul></ul><ul><li>Complex-Event Processing </li></ul><ul><ul><li>Process Optimization </li></ul></ul><ul><li>Data Quality Program </li></ul><ul><ul><li>Data Quality </li></ul></ul><ul><li>Knowledge Mining </li></ul><ul><ul><li>Knowledge Discovery </li></ul></ul>
    9. 9. Success Factors <ul><li>Entities and Events </li></ul><ul><ul><li>Well Defined </li></ul></ul><ul><ul><li>Clear Expectations </li></ul></ul><ul><ul><li>Precise Relations </li></ul></ul>
    10. 10. Semantic Web
    11. 11. The Web <ul><li>Created for Document Sharing </li></ul><ul><li>Focused on Presentation </li></ul><ul><li>Adapted for Human to Human </li></ul>
    12. 12. The Semantic Web <ul><li>Scientific American (2001) </li></ul><ul><li>Focused On </li></ul><ul><ul><li>Meaning </li></ul></ul><ul><ul><li>Knowledge Representation </li></ul></ul><ul><ul><li>Machine Consumption </li></ul></ul><ul><ul><li>Metadata </li></ul></ul><ul><li>«  Anybody can say Anything about Anything Anywhere  » </li></ul>
    13. 13. Syntax vs Semantic <ul><li>HTML and XML are syntax </li></ul><ul><li>Machine cannot extract “meaning” from the current Web. </li></ul>
    14. 14. Evoluation
    15. 15. Just Little Theory
    16. 16. Set theory
    17. 17. Function/Relation
    18. 18. Ressource Description Framework URI URI URI
    19. 19. Ressource Description Framework <ul><li>URIs are Surrogates for Things </li></ul><ul><li>Simple Statements </li></ul><ul><ul><li>Subject, Verb, Object (triple) </li></ul></ul><ul><li>Literals based in XSD types </li></ul><ul><li>Type is a standard Verb </li></ul><ul><ul><li>Uri and meaning </li></ul></ul><ul><li>XML and N3 are sterilization </li></ul>
    20. 20. RDF Schema <ul><li>Permits Information Schema Definitions </li></ul><ul><ul><li>Based on Set Theory and First-Order Logic </li></ul></ul><ul><li>Adds Subjects/Objects </li></ul><ul><ul><li>Resource, Class, Property </li></ul></ul><ul><li>Adds Verbs </li></ul><ul><ul><li>SubClassOf, Domain, Range </li></ul></ul><ul><li>Defines Entailment Rules </li></ul>
    21. 21. Web Ontology Language (OWL) <ul><li>Allows </li></ul><ul><ul><li>Schema Definitions (Description Logic) </li></ul></ul><ul><ul><li>Information Schema Alignment </li></ul></ul><ul><li>Adds Subjects/Objects </li></ul><ul><ul><li>Restriction </li></ul></ul><ul><li>Adds Verbs </li></ul><ul><ul><li>subProperty, Inverse, Transitive, etc. </li></ul></ul><ul><li>Defines Entailment Rules </li></ul><ul><li>OWL Lite, DL and Full </li></ul>
    22. 22. Example
    23. 23. Semantic Web Stack
    24. 24. Information Modeling
    25. 25. Meaning <ul><li>Natural Language is Ambiguous </li></ul><ul><li>Ambiguity can eliminated with Contextualization </li></ul><ul><li>Contextualization can be define through Relations </li></ul>
    26. 26. Perception <ul><li>One Reality, Multiple Views of It </li></ul><ul><li>Meaning is Relative to a Perception </li></ul><ul><li>Perception is Contextualization </li></ul>
    27. 27. Glossary vs Taxonomy
    28. 28. Ontology <ul><li>Hyper-taxonomy </li></ul><ul><ul><li>Multiple intersecting taxonomies </li></ul></ul><ul><li>Meaning is define with rich and complex relations </li></ul>
    29. 29. Modeling Technologies (key differences) Generalization by Restriction Disjoint Sub-Classing Multiple Inheritance XSD Schema Conceptual ER UML
    30. 30. Applications to EA
    31. 31. Modeling Language <ul><li>OWL is a (quasi) superset of traditional model languages </li></ul><ul><li>Non-Propriety file format </li></ul><ul><li>Offer Formal Verification </li></ul><ul><li>Offer Test-Driven Development </li></ul><ul><li>Analysis (SPARQL) </li></ul>
    32. 32. Model-Driven Data Specification <ul><li>Definitions (Glossary) </li></ul><ul><ul><li>Natural Language </li></ul></ul><ul><li>Ontology (OWL) </li></ul><ul><ul><li>Relation and Context </li></ul></ul><ul><li>Rules </li></ul><ul><ul><li>Expectation </li></ul></ul><ul><li>Alignment (CWM) </li></ul><ul><ul><li>Mapping </li></ul></ul>
    33. 33. Data Specification Governance <ul><li>“ Medium is the Message” </li></ul><ul><ul><li>Format is key </li></ul></ul><ul><li>Must be owned by the Business </li></ul>
    34. 34. Derived Artifacts <ul><li>Databases Schemas </li></ul><ul><li>XSD Schemas </li></ul><ul><li>OO Models </li></ul><ul><li>Cleansing Rules </li></ul><ul><li>Event Models </li></ul><ul><li>Knowledge Domain Models </li></ul>
    35. 35. Available Tools <ul><li>Editor : TopBraid Composer, Semanticworks </li></ul><ul><li>Storage : Oracle 10G </li></ul><ul><li>Relational To RDF : Virtuoso </li></ul><ul><li>Code : Jena, Linq To RDF </li></ul><ul><li>IA : Pellet, Racer </li></ul>
    36. 36. Back to Goals <ul><li>SOA </li></ul><ul><ul><li>Semantically Unambiguous XSD Schemas which are aligned with Relational Schemas </li></ul></ul><ul><li>Complex-Event Processing </li></ul><ul><ul><li>Semantically Unambiguous Event Models </li></ul></ul><ul><ul><li>Support of Event Inferencing </li></ul></ul><ul><li>Data Quality Program </li></ul><ul><ul><li>Governed Semantically Unambiguous Data Specification (Structure and Rules) </li></ul></ul><ul><li>Knowledge Mining </li></ul><ul><ul><li>Corporate Ontology which allow Knowledge Discovery </li></ul></ul>
    37. 37. Future Trends <ul><li>Semantic Databases </li></ul><ul><li>RDF based Enterprise Application Integration </li></ul><ul><li>Semantic Complex-Event Processing </li></ul><ul><li>Semantic Business Intelligence </li></ul><ul><li>Semantic Enterprise Information Integration </li></ul><ul><li>Enterprise Information Management </li></ul><ul><ul><li>Unified Model </li></ul></ul>
    38. 38. Questions

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