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By: M.Gillespie , H.Holmani , D. Kotowski, and D.A.Stacey Presented By: Daniel Kotowski

A Knowledge Identification Framework for the Engineering of Ontologies in System Composition Processes. By: M.Gillespie , H.Holmani , D. Kotowski, and D.A.Stacey Presented By: Daniel Kotowski dkotowsk @ uoguelph.ca. Who are we?. Guelph Ontology Team (GOT)

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By: M.Gillespie , H.Holmani , D. Kotowski, and D.A.Stacey Presented By: Daniel Kotowski

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  1. A Knowledge Identification Framework for the Engineering of Ontologies in System Composition Processes By: M.Gillespie , H.Holmani, D. Kotowski, and D.A.Stacey Presented By: Daniel Kotowski dkotowsk@uoguelph.ca

  2. Who are we? • Guelph Ontology Team (GOT) • Website: http://jaws.socs.uoguelph.ca • Soon to be: http://ontology.socs.uoguelph.ca • We have been recently established • Our Research Focus: • Semantic Web & Compositional Systems • Semantic Web & Workflow Planning • Semantic Web & Ontology Discovery and Reuse

  3. Goal of this Presentation • This paper is a position paper and preliminary work • We would like to start a dialog on the framework presented • To introduce aspects of an ODCS that needs to be considered when designing ontolgoies • Explore possible usage of the framework • We have done case study using this framework which will be presented at KEOD 2011 in Paris

  4. Outline • Introduction • Ontology Driven Compositional Systems (ODCS) • Current Implementations • Knowledge Identification Framework for ODCS • Categories of Knowledge Entities • Applications of Framework • Summary

  5. The Semantic Web & Compositional Systems • System Composition is the process of composing two or more previously implemented software and/or services to create a more functional system. • Note: We do not consider code “generation” • Compositional Systems are expert systems that automatically or semi-automatically perform system composition

  6. The Semantic Web & Compositional Systems • Compositional Systems required a knowledge base to reason which software/services are required to create the desired resultant system • Enter Ontologies!

  7. Ontology Driven Compositional System (ODCS) • An Ontology Driven Compositional System is reasons with ontological representations to construct a resultant system composed of compositional units Source Giliepse et. al. (2011)

  8. ODCS Examples:Semantic Web Services • Automatic Composition of Web Services • Ex. Arpinar et al. (2005) • WebService.owl • Process.owl • Domain.owl Source: Arpinar et al. (2005)

  9. ODCS Examples:BioSTORM Agent Composition • Automatic composition of syndromic surveillance software agents • DataSource.owl • SurveillanceMethods.owl • SurveillanceEvaluation.owl Source: Nyulaset.al. (2008)

  10. ODCS Examples:Algorithm Composition • Semi-automatic composition of Algorithms • Hlomani & Stacey (2009) • Algorithm.owl - Timeline.owl • Gillespie et al. (2011) • StatisticalModelling.owl • PopulationModelling.owl Kotowski et.al (2011)

  11. Let’s Not Reinvent the Wheel • Each system defines there own way to share knowledge • Often this method is unique to each system • However all these systems are trying to accomplish the same thing (even though they may be named different things) • Define Data architecture • Compositional Units • Workflow

  12. Wouldn’t it be Nice • Method for understanding what knowledge we needed to capture • To have a basis for evaluating our knowledge bases • There are elements systems do not capture but will be important as they evolve

  13. Knowledge Identification Framework Purpose: • Generalize knowledge entities within any type of ODCS • Propose collaborative vocabulary • Assist with Merging and Mapping between ODCS's ontologies • Enhance adaptability of future ontologies for ODCSs

  14. Knowledge Identification Framework Five Categories ofKnowledge: • Compositional Units • Work-flow • Data Architecture • Human Actors • Physical Resources

  15. Knowledge Identification Framework Internal vs.External: • Compositional Units • Work-flow • Data Architecture • Human Actors • Physical Resources

  16. Knowledge Identification Framework Internal vs.External: • Compositional Units • Work-flow • Data Architecture • Human Actors • Physical Resources

  17. Knowledge Identification Framework Syntactic vs SemanticKnowledge Entities: • Syntactic entities represent actual objects • Semantic entities represent the realization of those actual objects

  18. Knowledge Identification Framework Syntactic vs SemanticKnowledge Entities: • Like “Information Realization” ontology design pattern (Gangemi & Prescutti, 2009)

  19. Knowledge Identification Framework Semantic KnowledgeEntity Sub-Types: • Function • Data • Execution • Quality • Trust

  20. Examples of Knowledge Entities Compositional Unit Examples Syntactic: • Algorithm, Web Service, System Library Function, Input/Output Specification Semantic: subType::Function(i.e. Domain-specific actions) • Data aggregation/conversion/plotting/analysis, Statistical model, Aberrancy detection, etc. subType::Execution subType::Quality • Operating system Average Runtime

  21. Examples of Knowledge Entities Data Architecture Examples Syntactic: • Single Datum, Structured Data, Data Source, Data Set Semantic: • subType:Data • Data Context, Data Context Component • DataSource Structure, DataSource FileFormat • Data Structure (i.e., Matrix, Vector, Variable) • Data Type • Units of Measure

  22. Examples of Knowledge Entities Human Actor Examples Syntactic: • Person, Organization, Recommendation Semantic: subType: Trust • Role (i.e., software developer, domain-expert, novice-user) • Recommendation Context • Organization Type • Organization Governance

  23. Knowledge Identification Framework Relationships betweenKnowledge Categories • Syntactic Relationships • Semantic Relationships

  24. Relationships between Knowledge Categories Syntactic Relationship Example ---- ---- Human Actor Data Architecture Data Architecture Compositional Unit Compositional Unit Data Source requires Input Specification Input Specification has_input Algorithm contains Datum sameAs can_use contains Data Source Person owns

  25. Relationships between Knowledge Categories Semantic Relationship Example (Function & Trust) ---- Human Actor Compositional Unit Input Specification SpaceTime Dimension has_feature Algorithm recommends Person trusts_ using works_in OrganizationalRole trusts Person

  26. Applications ofFramework Ontology Evaluation using Software Quality Assurance Checklist • With “SQA-like” Checklist, evaluated the adaptability of the BioSTORM ontologies

  27. Applications ofFramework Ontology Capture & Integration • Adapting current knowledge representations to improve ontologies for Algorithm construction: Hlomani & Stacey (2009) Gillespie et al (2011) SystemComposition.owl imported_by HumanActors.owl PhysicalResources.owl DataArchitecture.owl Workflow.owl CompositionalUnits.owl FOAF.owl Process.owl (ISO) Time.owl (W3C) DataSource.owl (BioSTORM) Algorithm.owl (Hlomani)

  28. Summary • Knowledge Identification Framework assists: • With the capture of knowledge about components of an ODCS • Detailing relationships between the categories of knowledge • Both syntactic and semantic • Merging and mapping between ODCS’ ontologies • Enhance adaptability of future ontologies for ODCS’

  29. Thank You!!

  30. References • Arpinar, I. B., Zhang, R., Aleman-Meza, B., & Maduko, A. (2005). Ontology-driven Web services composition platform. Information Systems and e-Business Management, 3(2), 175-199. doi:10.1007/s10257-005-0055-9 • Gillespie, M. G., Stacey, D. A., & Crawford, S. S. (2011). Designing Ontology-Driven System Composition Knowledge and Processes to Satisfy User Expectations (in publication). Communications in Computer and Information Science (CCIS). Springer-Verlag. • Hlomani, H., & Stacey, D. A. (2009). An ontology driven approach to software systems composition. International Conference of Knowledge Engineering and Ontology Development (pp. 254-260). INSTICC. • Nyulas, C. I., O’Connor, M. J., Tu, S. W., Buckeridge, D. L., Okhmatovskaia, A., & Musen, M. a. (2008). An Ontology-Driven Framework for Deploying JADE Agent Systems. 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 573-577. Ieee. doi:10.1109/WIIAT.2008.25 • Kotowski, D, Heriques, G., Gillespie,M., Hlomani,H., & Stacey,D (2011). Leveraging User Knowledge: Design Principles for an Intuitive User Interface for Building Workflows. KEOD 2011. • Holmani, H., Gillespie, M., Kotowski, D., Stacey,D.(2011). Utilizing a Compositional System Knowledge Framework for Ontology Evaluation: A Case Study on BioSTORM

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