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

transcript

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

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

Outline

Introduction

Ontology Driven Compositional Systems (ODCS) Current Implementations

Knowledge Identification Framework for ODCS Categories of Knowledge Entities

Applications of Framework

Summary

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

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!

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)

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)

ODCS Examples:BioSTORM Agent Composition

Automatic composition of syndromic surveillance software agents

DataSource.owl SurveillanceMethods.owl SurveillanceEvaluation.owl

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

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

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

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

Five Categories of Knowledge:

Compositional Units

Work-flow

Data Architecture

Human Actors

Physical Resources

Internal vs. External:

Compositional Units

Work-flow

Data Architecture

Human Actors

Physical Resources

Internal vs. External:

Compositional Units

Work-flow

Data Architecture

Human Actors

Physical Resources

Syntactic vs Semantic Knowledge Entities:

Syntactic entities represent actual objects

Semantic entities represent the realization of those actual objects

Syntactic vs Semantic Knowledge Entities:

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

Semantic Knowledge Entity Sub-Types:

Function

Execution

Quality

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

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

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

Relationships between Knowledge Categories

Syntactic Relationships

Semantic Relationships

Syntactic Relationship Example

AlgorithmInput Specification

has_input

Compositional UnitData Architecture Compositional UnitData ArchitectureHuman Actor ----

Input Specification

Data Source

requires

sameAs

contains

Personowns

can_use

Semantic Relationship Example (Function & Trust)

AlgorithmInput Specification

has_feature

Compositional UnitHuman Actor

SpaceTimeDimension

Person

Personworks_in

trusts_ using

OrganizationalRole

trusts

recommends

Applications ofFramework

Ontology Evaluation using Software Quality Assurance Checklist

– With “SQA-like” Checklist, evaluated the adaptability of the BioSTORM ontologies

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)

Summary

• Knowledge Identification Framework assists:• With the capture of knowledge about components of an

• 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!!

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

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

Documents