1. Knowledge Management in Geodise Geodise Knowledge Management Team Liming Chen, Barry Tao, Colin Puleston, Paul Smart University of Southampton University of Manchester Epistemics Ltd.

2. Overview Geodise needs knowledge management Knowledge acquisition and modelling Grid-oriented knowledge management Knowledge applications in Geodise v Creating semantic content v Workflow management v Knowledge-based advice v EDSO component management Summary and future work

3. Geodise Meets Knowledge Management (KM) - put KM in context -

4. Geodise will provide grid-based seamless access to an intelligent knowledge repository, a state-of-the-art collection of optimisation and search tools, industrial strength analysis codes, and distributed computing & data resources G EODISE

5. The Problems & the Solutions Geodise: “Flexible and secure sharing of resources on the Grid to carry out Engineering Design Search and Optimisation (EDSO) v Component level - EDSO tasks such as problem setup, mesh generation, code analysis, DOE, RSM, Optimisation, etc. v Process level – EDSO workflow for problem-solving v Grid level - resource accessibility, sharing, reuse, interoperability, etc. The problems  From “infosmog” to shared, semantically enriched, well-structured knowledge repositories  From standalone KBSs to knowledge services on the Grid The solutions  Ontology – conceptual backbone for resource sharing and creating semantic content  Knowledge management – knowledge delivery, reuse and decision-making support

6. The Approach to Knowledge Management Domain Users Knowledge Engineers Domain Experts Application Domain Application Scenarios & User Requirements Knowledge Acquisition Knowledge Publishing Knowledge Modelling Knowledge Use & Re-use Knowledge Maintenance Validation Knowledge Support Via KBSs Application Systems

7. Knowledge Acquisition and Modelling - what we need & how to get them -

8. Knowledge Acquisition (KA) Knowledge sources Domain experts, software manuals & textbooks. KA techniques Interview, protocol analysis, concept sorting etc. Tools used PC-PACK integrated knowledge engineering toolkit Knowledge acquired EDSO domain knowledge, EDSO processes and problem definition Concept mark-up in Protocol Editor Concept hierarchy in Laddering Tool

9. Knowledge Modelling Techniques CommonKADS knowledge engineering methodologies. Knowledge models Organization, agent & task templates, domain schema & inference rules. Tools used PC-PACK integrated knowledge engineering toolkit Deliverables Knowledge web in HTML, XML and UML, Conceptual task model, EDSO process flowchart

10. Ontology Development (1) Tools Protégé & OilEd Editor Representation DAML+OIL & CLIPS Deliverables  EDSO domain ontology  EDSO task ontology  Mesh generation tool (Gambit software) ontology  User-profile ontology Protégé Editor OilEd Editor DAML+OIL

11. Ontology Development (2) Ontology Views DL ontologies (DAML/OWL) Simplified views Tailored to specific domains Other Views Other Views?? Ontology Client Ontology Server WEB Semantic Network View (Configurable) DAML+OIL/OWL Ontology Instance Store (Database) Geodise Tasks Geodise Concept s FaCT Reasoner GONG Concept s Concept Query View Ontology Views Underlying complexity hidden Ontology editing by…  Knowledge engineers  Domain experts

12. Grid-oriented Knowledge Management - From local, standalone KBSs to distributed, shared knowledge services -

13. Features: Service-oriented approach Ontologies as a conceptual backbone Integrated KM framework Layered modular structure Distributed knowledge reuse & sharing Flexible & extensible Robust & easy maintenance The KM Architecture for the Grid

14. Knowledge Portal Functions  Make knowledge available & accessible  Provide tools for knowledge reuse and exchange  Security infrastructure  Knowledge resources management Techniques  Microsoft.Net framework

15. Ontology Services Facilitating ontology sharing & reuse  Ontology service APIs Domain independence  DAML+OIL/OWL standards Soap-based web services -WSDL Java, Apache Tomcat & Axis technologies

16. Knowledge Advice Service Application Side  Ontologies  Knowledge bases  Problems being solved Knowledge Service Side  Inference layer: the reasoning process of a KBS in domain-independent terms  Communication layer: XML-based messaging  Application layer: provide common terms for knowledge bases, inference layer and communication schema Standalone knowledge advice system implemented Not wrapped as web/Grid service yet

17. Exploiting Knowledge in Geodise - Make differences for EDSO through the use of knowledge -

18. Knowledge Application 1: Create Semantic Content Goals  Machine understandable information  Facilitate sharing & reuse Technique & tool  OntMat-annotizer  Geodise Ontologies Example  OPTIONS log-files annotation

19. Knowledge Application 2: Ontology-assisted Workflow Management Features:  Function selection  Function instantiation  Database schema  Semantic instances  Semantic workflow Technologies:  EDSO ontologies & ontology services  Java JAX-RPC, DOM/SAX

20. Knowledge Application 3: Knowledge-based Design Advisor Features  Context-sensitive advice  Advice at multi-levels of granularity (process, task … )  KBSs as knowledge services Technologies  Knowledge engineering  EDSO ontologies  Rule-based reasoning techniques

21. Knowledge Application Prototype Knowledge-based Ontology-assisted Workflow Construction Environment

22. Knowledge Application 4: EDSO Component Management for the Grid Aim – to make EDSO components (which could be a problem definition, an algorithm, a solution or a task) available on the Grid, easy of use and reusable to other users. Problems involved  Describe or model components in a way …  Create instances and repositories  Discovery and retrieval mechanisms  Query and inference mechanisms  Semantics on the use and re-use of the components

23. Knowledge Application 4: Component Management (1) XML-based Template-oriented Approach Use XML & XML Schema Java/JAXFront technology Access via knowledge APIs Potential ontology support

24. Example Use – Arcadia Problem Setup Knowledge API called in MatLab

25. Semantic description for components using DAML+OIL /OWL ontologies Automated form generation for creating instances RDF as the representation formalism Semantic knowledge repository using RDF triple store Semantics-based query & inference technologies EDSO Ontologies (service/function) Ontology Services Service/Function Form or Templates Semantics-based Query & Inference RDF Triple Store & Permanent Storage (DBS) Concept Java Classes RDF Generator Jena RDF APIs Geodise Users Create Re-use Geodise toolkit in Matlab Knowledge Application 4: Component Management (2) Semantic Service-oriented Approach

26. Summary EDSO knowledge  EDSO domain, process, problem definition, (partial) optimisation algorithms EDSO ontologies  Domain ontology, task ontology, Gambit & user profile ontology Grid-oriented knowledge management architecture  Ontology service infrastructure  Knowledge publishing mechanism  Service-oriented KBS paradigm Application prototypes  Knowledge portal; workflow construction environment; knowledge-based advice system, XML-based templates-oriented description for EDSO components; ontology-assisted Gambit Journal file editor A semantic description framework for EDSO components

27. Future Work Component management  Knowledge repositories for EDSO functions, problems in CFD & workflows …  Storage, query & inference mechanisms Service-oriented KBSs reuse infrastructure  Reasoning services - problem-solving methods (PSM)  Brokering services - a paradigm for manipulating reasoning services on the Web Knowledge-based decision-making support systems  Knowledge intensive points (need to be clarified from domain users)  Further KAs  Semantics-based, case-based reasoning mechanisms Geodise knowledge toolkit in Matlab  Where & when it fits in, what knowledge is needed, in which form? We need application scenarios & user requirements.

28. Thank you! Q/A …

29. Knowledge Application 2: Ontology-assisted Workflow Management Features  Ontology-assisted function selection  Ontology-assisted function instantiation  Database schema  Semantic instances & workflow Ontology service Task ontology Technologies  EDSO ontologies & ontology services  Java JAX-RPC, DOM/SAX

30. 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