by: m.gillespie , h.holmani , d. kotowski, and d.a.stacey presented by: daniel kotowski
DESCRIPTION
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) - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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
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Outline Introduction Ontology Driven Compositional Systems (ODCS)
Current Implementations Knowledge Identification Framework for ODCS
Categories of Knowledge Entities Applications of Framework Summary
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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
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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!
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Ontology Driven Compositional System (ODCS)
An Ontology Driven Compositional System is reasons with ontological representations to construct a resultant system composed of compositional units
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Source Giliepse et. al. (2011)
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ODCS Examples:Semantic Web Services
Automatic Composition of Web Services
Ex. Arpinar et al. (2005) WebService.owl Process.owl Domain.owl
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Source: Arpinar et al. (2005)
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ODCS Examples:BioSTORM Agent Composition
Automatic composition of syndromic surveillance software agents
DataSource.owl SurveillanceMethods.owl SurveillanceEvaluation.owl
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Source: Nyulas et.al. (2008)
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ODCS Examples:Algorithm Composition
Semi-automatic composition of Algorithms
Hlomani & Stacey (2009) Algorithm.owl -
Timeline.owl Gillespie et al. (2011)
StatisticalModelling.owl PopulationModelling.owl
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Kotowski et.al (2011)
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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
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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
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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
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Knowledge Identification Framework
Five Categories of Knowledge:
Compositional Units Work-flow Data Architecture Human Actors Physical Resources
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Knowledge Identification Framework
Internal vs. External:
Compositional Units Work-flow Data Architecture Human Actors Physical Resources
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Knowledge Identification Framework
Internal vs. External:
Compositional Units Work-flow Data Architecture Human Actors Physical Resources
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Knowledge Identification Framework
Syntactic vs Semantic Knowledge Entities:
Syntactic entities represent actual objects
Semantic entities represent the realization of those actual objects
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Knowledge Identification Framework
Syntactic vs Semantic Knowledge Entities:
Like “Information Realization” ontology design pattern (Gangemi & Prescutti, 2009)
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Knowledge Identification Framework
Semantic Knowledge Entity Sub-Types:
Function Data Execution Quality Trust
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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
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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
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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
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Knowledge Identification Framework
Relationships between Knowledge Categories
Syntactic Relationships Semantic Relationships
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Relationships between Knowledge Categories
Syntactic Relationship Example
AlgorithmInput Specification
has_input
Compositional UnitData Architecture Compositional UnitData ArchitectureHuman Actor ----
Input Specification
Data Source
Data Source
Datum
requires
sameAs
contains
contains
Personowns
can_use
----
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Relationships between Knowledge Categories
Semantic Relationship Example (Function & Trust)
AlgorithmInput Specification
has_feature
Compositional UnitHuman Actor
SpaceTimeDimension
Person
Personworks_in
trusts_ using
----
OrganizationalRole
trusts
recommends
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Applications ofFramework
Ontology Evaluation using Software Quality Assurance Checklist
– With “SQA-like” Checklist, evaluated the adaptability of the BioSTORM ontologies
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Applications ofFramework
Ontology Capture & Integration
SystemComposition.owl
DataArchitecture.owl
HumanActors.owlPhysicalResources.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)
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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’
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Thank You!!
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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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