© Geodise Project, University of Southampton, 2002. Knowledge Management in Geodise Geodise Knowledge Management Team Barry Tao, Colin Puleston, Liming Chen, Paul Smart University of Southampton University of Manchester Epistemics Ltd.
Jan 14, 2016
© Geodise Project, University of Southampton, 2002.
Knowledge Managementin Geodise
Geodise Knowledge Management Team
Barry Tao, Colin Puleston, Liming Chen, Paul Smart
University of SouthamptonUniversity of Manchester
Epistemics Ltd.
© Geodise Project, University of Southampton, 2002.
Distinguishing features of the architecture:Tackling the six challenges of knowledge management life cycle in an integrated framework.A layered modular structure with each component dealing with a specific aspect of knowledge engineering process.Supporting the exploitation of different techniques and tools. Each of them can be updated while others kept intact. Using ontologies to generate machine-interpretable semantically-enriched content and also knowledge bases.A knowledge portal provides mechanisms for knowledge maintenance and resource control.
Easy knowledge reuse and sharing through web service technology over the Internet.Flexible - adding knowledge any time through portal.
Service-oriented approach - separating domain knowledge from operational knowledge.
Extensible - plugging in new functions as knowledge services.Robust - adopting new techniques/tools while keeping the system functioning.
While computing increasingly addresses collaboration, sharing and interaction with the powerful support of the emerging distributed computing infrastructures such as web services and Grid technologies, there is a growing demand for ontology and knowledge technologies to provide semantic support for service integration, the sharing and coordinated use of knowledge across distributed, heterogeneous, dynamic virtual organisations. We have developed and partially implemented an architecture to provide knowledge services. It has been applied to Grid-enabled design search and optimisation (Geodise).
Knowledge Management Architecture for Grid Computing
© Geodise Project, University of Southampton, 2002.
Knowledge acquisition and modelling lay the foundation for the knowledge service architecture, which produces ontologies and the types of template and structure where knowledge can be held. It has been carried out by the CommonKADS knowledge engineering methodology and the PC PACK knowledge management integrated tools.
Concept LadderProtocol Editor
Diagram Tool
The aim of knowledge portal is:To make knowledge available and accessibleTo provide tools for knowledge reuse and exchangeTo provide security infrastructureTo manage knowledge resourceTo support online forum and maintain mailing listsTo disseminate the latest advance of the domain
Current functions of knowledge portal include:Security mechanismKnowledge publicationService registrationKnowledge retrieval and updateService application informationResource management via version control
Security Control
Knowledge PortalBrowser-based Interface
© Geodise Project, University of Southampton, 2002.
Knowledge representation and publishing is to represent knowledge in a well-structured, well-indexed form and make them ready for sharing and use. Geodise domain knowledge has been represented in CommonKADS task models, concept hierarchy and a design workflow and published in the form of knowledge web and XML format.
DesignWorkflow
Knowledge Web
Knowledge Web in XML
Geodise Domain Models
Ontologies serve as the conceptual backbone for knowledge sharing and management in the above integrated architecture for knowledge services.We have developed ontologies for design search and optimisation using both Protégé and OilEd ontology tools.
Protégé Ontology Concept Instances
Protégé Ontologyin HTML Format
Protégé OntologyConcept Hierarchy
OilEd Ontology in DAMLClasses, Hierarchy & Properties
Geodise Knowledge Models
© Geodise Project, University of Southampton, 2002.
Ontology service provides a Java API giving full access to any DAML+OIL ontology that is available over the Internet. The API is exposed through both a SOAP-based web service and a CGI interface for common ontological operations, such as subsumption checking, navigating concept hierarchy and retrieving concept and attributes.
Ontology Service CGI Interface
SOAP-based Ontology Service WSDL
Ontology ServicePresentation in Portal
Annotation is necessary to add semantic content to document or website, thus facilitating information sharing, reuse and automatic machine processing. We have used OntoMat-Annotizer annotation tool in Geodise to annotate the workflow for particular design problems and then save them in a knowledge base. The semantically enriched archive can then be queried, indexed and reused later to guide future designs.
Annotation Process
Original Document
Metadata Added after Annotation
Ontology Services &Semantic Annotations
© Geodise Project, University of Southampton, 2002.
Knowledge-based Ontology-assisted Workflow Construction ArchitectureWorkflow construction
Use ontology & ontology services to facilitate method selection & instantiation
Design advice Use process knowledge
& advice reasoner to provide design advice
Storage & query Add semantics to
databases & workflow automatically, thus facilitate content-based search and query
© Geodise Project, University of Southampton, 2002.
Ontology service
Ontology-driven Workflow Construction Ontology-assisted task navigation & selection (Drag and Drop) Ontology assisted task configuration
Task ontology
© Geodise Project, University of Southampton, 2002.
Knowledge-based Systems for EDSO
Gambit journal
file editor
Knowledge-based advisor
Design advice
Add a task
Process-level design advisor Service-oriented paradigm Ontology as common terms
Task-level design tools Ontology-assisted Gambit journal file
editor Critique on commands & workflow
Knowledge APIs XML-based messaging
© Geodise Project, University of Southampton, 2002.
% Query and locate the instance fileresult=gd_query('standard.archiveDate > 2003-03-16');ProblemID=result{1}.standard.ID;local_file_path=gd_retrieve(ProblemID,'d:\'); %local_file_path=’D:/geodise/XML_Templates/problem_mo_arcadia5.xml’
% Specify the local path of the problem profile instance.problem_profile_instance=local_file_path;
% get information about design variablesxp=knowledgeapi.XMLParser(problem_profile_instance);% get information about design variablesdvs=knowledgeapi.DesignVariables1(xp.getDoc);%get information about objective functionof=knowledgeapi.ObjectiveFunction(xp.getDoc);% The recommented boundaries for the design parameters, useful as% allows the user to use a constrained optimisation.% design parameter boundsdsgnmin = rot90(dvs.getLowerBounds);%[ -0.15 0.40 2.00 0.00 0.50 2.00];dsgnmax = rot90(dvs.getUpperBounds);%[ 0.05 0.80 10.00 0.15 0.85 5.00];defaultValues=rot90(dvs.getDefaultValues);% design parameters selected to be design variablesselect = rot90(dvs.getSelected);%[3,5];selectedObjName=char(of.getSelectedObjName);
% Create a setup file for the optimisation% [setup_struct,setupFileID] = arcadia5_setup( [3,5],'peakvel2','',[0.04,0.5,3,0.02,0.6,3],1.4,1.5,0.1,4.6,[ -0.75,1.5,0,0.95],[0.05,0.0125,0.05,0.0125])[setup_struct,setupFileID] = arcadia5_setup( select,selectedObjName,'',defaultValues,1.4,1.5,0.1,4.6,[ -0.75,1.5,0,0.95],[0.05,0.0125,0.05,0.0125])
% Example use of the programDesignVariables= rot90(dvs.getSelectedDefaults) %[2.0, 0.85];
XML-Based Component Management XML & XML Schema
Java/JAXFront technology
Access via knowledge APIs
Potential ontology support Visualisati
on Implementation
Framework
<ProblemProfile description="Arcadia5 design problem" dg_id="" lastTimeUsed="2003-03-04T11:21:36" timeCreated="2002-11-23T09:20:23"user="barry" xmlns="http://www.geodise.org/knowledge" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.geodise.org/knowledgeD:\geodise\XML_Templates\problem_mo.xsd">
<designVariables><name>a_l</name><meaning>The maximum bump height of the Hicks-Henne bump function on the lower surface of the nacelle</meaning><unit>mm</unit><limit>
<continousLimit><lower_bound>-0.15</lower_bound><default_value>0.05</default_value><upper_bound>0.04</upper_bound>
</continousLimit></limit><fixed>true</fixed>
</designVariables><designVariables>
<name>xp_l</name><meaning>The bump peak location (on the x-axis) of the Hicks-Henne bump function on the lower surface of the nacelle</meaning><unit>mm</unit><limit>
<continousLimit><lower_bound>0.40</lower_bound><default_value>0.5</default_value><upper_bound>0.80</upper_bound>
</continousLimit></limit><fixed>true</fixed>
</designVariables><designVariables>
<name>t_l</name><meaning>The bump width parameter of the Hicks-Henne bump function on the lower surface of the nacelle</meaning><unit>mm</unit><limit>
<continousLimit><lower_bound>2.00</lower_bound><default_value>3</default_value><upper_bound>10.00</upper_bound>
</continousLimit></limit><fixed>false</fixed>
</designVariables><designVariables>
<name>a_u</name><meaning>The maximum bump height of the Hicks-Henne bump function on the upper surface of the nacelle</meaning><unit>mm</unit><limit>
<continousLimit><lower_bound>0.00</lower_bound>
Problem
Definitio
n
Design
Variable
DefinitionProblem Instance in
XML
Use Problem Instances
in Matlab
Framework
© Geodise Project, University of Southampton, 2002.
Semantic Component Management
PSE:Workflow Construction
Functions in .m files Matlab ExecutionEnvironment
Semantic DescriptionUsing DAML-S
Enactment or Mapping to .m Scripts
Ontology Services
Resource annotations
Geodise ServiceOntology
Geodise Domain Ontologies
Knowledge RepositoriesWith Embedded Semantics
(RDF Triple Store)
Inference engines
Retrieval andQuery APIs
To knowledge-enable
EDSO Ontologies (service/function)
Ontology Services
Service/FunctionForm or Templates
Semantics-based Query & Inference
Semantics-based Query & Inference
RDF Triple Store& Permanent Storage (DBS)
Concept Java Classes
RDF GeneratorJena
RD
F A
PIs
Geodise Users
Create
Re-use
Geodise toolkit in Matlab
• Common interface• Semantic content• Machine understandable• Flexible discovery & composition
Service-oriented semantic descriptions
Automated form generation
RDF as the representation formalism
RDF triple store & DBMS
Semantic & knowledge technologies
Implementatio
n framework
© Geodise Project, University of Southampton, 2002.
Ontology Delivery
Classification
Language
Indexing
FaCT
Terminology Service
OilEd
User Interfacetoolkits
Browser & viewers,Query builders,Form builders,Wizards
Annotation & Linking(COHSE)
Instance Store
ViewsMatcher
Core OntologyService (OS)
© Geodise Project, University of Southampton, 2002.
Ontology Building Environments
Knowledge acquisitionOntology creation and maintenanceOntology parsers and checkersOntology alignment and mergingOntology reasoning
© Geodise Project, University of Southampton, 2002.
Ontology View FrameworkOtherViews
Other Views??
Ontology Client
Ontology Server
WEB
Semantic Network View (Configurable)
DAML+OIL/OWL Ontology
Instance Store (Database)
GeodiseTasks
Geodise Concepts
FaCTReasoner
GONGConcepts
Concept Query View
© Geodise Project, University of Southampton, 2002.
Geodise Task Ontology View
Ontology Client
API
GUI
Geodise Task View
GeodiseWorkflow Application
Semantic Network View
Semantic NetConfiguration
Semantic NetAccess
Configured for Geodise task ontology
Configured for Geodise task
ontology