1. 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.cahttp://jaws.socs.uoguelph.ca Soon to be: http://ontology.socs.uoguelph.cahttp://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 2

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 3

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

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 5

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 ! 6

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 7 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 8 Source: Arpinar et al. (2005)

9. ODCS Examples: BioSTORM Agent Composition Automatic composition of syndromic surveillance software agents DataSource.owl SurveillanceMethods.owl SurveillanceEvaluation.owl 9 Source: Nyulas et.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 10 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 11

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 12

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 13

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

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

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

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

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

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

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 20

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 21

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 22

23. Knowledge Identification Framework Relationships between Knowledge Categories Syntactic Relationships Semantic Relationships 23

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

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

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

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

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’ 28

29. Thank You!! 29

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 30