Closing the Semantic Gaps in Topic Maps and OWL ontologies with Modelling Workflow Patterns

1. Closing the Semantic Gaps in Topic Maps and OWL ontologies with Modelling Workflow Patterns Lutz Maicher, Martin Böttcher University of Leipzig maicher@informatik.uni-leipzig.deboettcher@informatik.uni-leipzig.de

2. Agenda Idea - Examples - Definition „Semantic Gap“ - Basic Solution Realisation - Basic Realisation - Data- and Processing Model - Workflow example Implementation - OWL Example Outlook Lutz Maicher, Martin Böttcher

3. Created in background: Model which represents in Dublin Core Terms, that Lutz Maicher is the creator of the content of www.tmra.de Lutz Maicher, Martin Böttcher

4. Idea Lutz Maicher, Martin Böttcher

5. OWL Example Only application dependent support Comprehensive Ontologies User Roles Use(Instantiation) Creation How can this Information be preserved? How can these Problems be solved? Use Cases Need of a use case or role specific view Creation of an Ontology for employees. Roles: Secretary, Manager, HR Use Cases: „new employee“, „getting information“, „changing salary“ Use of an Ontology for employees. Roles: Secretary Use Cases: „new employee“ Problems: What needs to be added? Where to get the information? Lutz Maicher, Martin Böttcher

6. www.tmra.de www.isotopicmaps.org www.tmra.de dc:creator Lutz Maicher dc:contributor L.M. Garshol dc:creator Lutz Maicher dc:subject Topic Maps dc:subject Topic Maps www.uni-leipzig.de/~maicher dc:subject Topic Maps www.uni-leipzig.de/~maicher www.isotopicmaps.org dc:creator Lutz Maicher dc:contributor L.M. Garshol dc:subject Topic Maps (Autonomous) Topic Maps Example Modelling method has to be broadcasted. Lutz Maicher, Martin Böttcher

7. Semantic Gap The usage of one ontology allows the creation of various independent model types, due to the interpretation spaces left by providing only the vocabulary. Lutz Maicher, Martin Böttcher

8. Workflows for Representing Modelling Methods Closing the semantic gap means to define the intensions of each model type by the description of the modelling method to apply. A modelling method is a workflow which describes how observation of subjects should be documented with the given ontology. Lutz Maicher, Martin Böttcher

9. Realisation Lutz Maicher, Martin Böttcher

10. Basic Realisation Design rationales of Modelling Workflow Patterns: Workflow based Grounding on Petri nets Self containedness Generic representation Mapping Petri net Data Model Proprietary workflow representation Generic Interpreter Topic Maps SyntaxOWL Syntax Petri net Processing Model Lutz Maicher, Martin Böttcher

11. Data- and Processing Model Petri net Data Model Petri net Processing Model The Petri net data model (PNDM) is derived from the formal specification of Petri nets. The constraints how the Interpreter has to process these Petri Nets are defined by the Petri net Processing Model (PNPM). The PNDM and PNPM allow to properly represent any kind of Petri Nets with these syntaxes, including MWPs. Specification of PNDM and MWP PNPM available at: http://www.informatik.uni-leipzig.de/~maicher/mwp/mwp.htm Lutz Maicher, Martin Böttcher

12. Example of a workflow Roles: Secretary Use Cases: „ add a new employee“ Operator: hum.bin.req. Operand: „Is there a new employee?“ Operator: hum.quest. Operand: „Ask for the name!“ Operator: ont.request Operand:„Employee exists?“ Operator: hum.bin.req. Operand: „Add the employee?“ Operator:ont.update Operand: „Insert employee“ yes no yes no yes no Operator:hum.inf. Operand:„Employee exists!“ Lutz Maicher, Martin Böttcher

13. Representation of the example workflow as Petri net [id] t3r42 [characteristic] {([key] result.id1 [value] Smith)} using s for http://psi.semports.org/MWP# [id] id2 [operator] s:SPARQL_binary [operand] SELECT ?person WHERE {?person rdfs:type db:Person. ?person db:name %result.id1%} [id] id1 [operator] s:human_string [operand] What is the employee‘s name? conditions: null conditions: null [id] id3 [operator] s:human_binaryDecision [operand] Shall the employee be added? conditions: %id2.value% = s:TRUE [id] id4 [operator] s:human_information [operand] Employee already exist. [id] t3r42 [characteristic] {[key] result.id1 [value] Smith), ([key] result.id2 [value] s:TRUE)}] conditions: %id2.value% = s:FALSE Lutz Maicher, Martin Böttcher

14. Implementation Lutz Maicher, Martin Böttcher

15. Implementation (MWP Factory) Editor (Protégé Plug-In) Lutz Maicher, Martin Böttcher

16. OWL Example – Complexity of Ontologies Lutz Maicher, Martin Böttcher

17. OWL Example – Protégé plug-in for MWP Lutz Maicher, Martin Böttcher

18. OWL Example – MWP Factory Lutz Maicher, Martin Böttcher

19. Outlook Lutz Maicher, Martin Böttcher

20. Outlook • Validation • validation of workflow definitions • ex-ante validation of the models to be created (based on the workflow definition) • Workflow Patterns • representing common tasks in plugable workflow patterns • Interpreter for different usage contexts • small footprint, web-based, mobile environments Lutz Maicher, Martin Böttcher