By: M.Gillespie, H.Holmani, D. Kotowski, and D.A.Stacey Presented By: Daniel Kotowski [email protected]

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

[email protected]

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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http://jaws.socs.uoguelph.ca/

http://ontology.socs.uoguelph.ca/

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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Five Categories of Knowledge:

Compositional Units

Work-flow

Data Architecture

Human Actors

Physical Resources

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Internal vs. External:

Compositional Units

Work-flow

Data Architecture

Human Actors

Physical Resources

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Internal vs. External:

Compositional Units

Work-flow

Data Architecture

Human Actors

Physical Resources

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Syntactic vs Semantic Knowledge Entities:

Syntactic entities represent actual objects

Semantic entities represent the realization of those actual objects

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Syntactic vs Semantic Knowledge Entities:

Like “Information Realization” ontology design pattern (Gangemi & Prescutti, 2009)

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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 Runtime

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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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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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Relationships between Knowledge Categories

Syntactic Relationships

Semantic Relationships

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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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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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Documents

By: M.Gillespie, H.Holmani, D. Kotowski, and D.A.Stacey Presented By: Daniel Kotowski [email protected]