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)

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)

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)

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)

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)

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

Runtime20

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’

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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