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1 Universität Karlsruhe (TH) Forschungsuniversität gegründet 1825 Improving Automatic Model Creation using Ontologies Sven J. Körner, Tom Gelhausen Institute for Program Structures and Data Organization Chair of Prof. Dr. Walter F. Tichy University of Karlsruhe, Germany

2 Ontologies Improve Automatic Model Creation Automatic model creation is possible through adding semantics Automatic model creation has potential benefit from textual specifications ( pages) Considers (implicit) semantics; embeds common sense Moves manual step to automatic/mechanical steps Less-error prone model creation then humans Repeatable Deterministic 2

3 Software Development Today 3

4 Supported Requirements Engineering Tool Support Customer specification Requirements/functional specification Code stub-generation from models Problem Model creation from specification Manual task to create models 4

5 Automatic Model Creation an Overview Add semantics Code (Specs + Annotations) Process semantics Transform Internal Model 5

6 Customer Specifications Start with Text Thematic Relation: AGENS Thematic Relation: ACTUS The game of chess is played between two opponents. The player with the white pieces commences the game 6

7 Annotating Text [ The game of chess is played ACT between two opponents AG ]. [ The player AG with the white pieces commences ACT the game ]. 7

8 Annotated Text in the SENSE/SAL E System [ #The game_of_chess PAT #is played ACT #between *two opponents AG ]. [ [ The ^player POSS #with #the $white pieces HAB ] AG commences ACT #the game PAT ]. 8

9 Limitations of Current Solution Manual annotations Model creation needs common sense Conceptual relatedness GameOfChess -isplayed Opponents 1 2 Player +commence(in GameOfChess) Choosing the right kind of model 9

10 Related Work Gelhausen: Automatic model creation SENSE/SAL E Meystre/Haug Automatic annotation and natural language processing Specialized ontologies work in small domains Bethard et al. Timeline extraction Timely dependencies of textual artifacts Liu/Lieberman High coherence between natural language and code NLP with students ConceptNet (special trained ontology) 10

11 Improve Model Creation with Ontology Add semantics Ontology Build Graph Transform 11

12 Architecture Including the Ontology Text 1 Annotated SAL E -Text Graph Ontology List of AGs (player, opponent) List of ACTs (plays, ) 5 Rules 4 UML 12

13 UML Representation for AG Are Opponents and Player related? Agens (AG) A compared to Agens (AG) A B B C... C 13

14 Decreasing Complexity of UML models with Ontologies Earlier UML model GameOfChess -isplayed Opponents 1 2 Player +commence(in GameOfChess) New, improved UML model using ontologies GameOfChess -isplayed Player -isopponent 1 2 +commence(in GameOfChess) 14

15 Thematic Relation Explanation UML model element AG ACT PAT HAB Thematic Relations Matched to UML The acting person or thing executing the action The action, executed by a person or thing Person or thing affected by the action or on which the action is being performed Possession or belonging; person or thing being received or passed on by person or thing Class- Role, or Instance Method, State- (Transition), or Relation Class, Role, or Instance Class, Role, or Instance 15

16 UML Representations for ACT instantanious modeled as state-(transition) State A TransitionACT State B eternal modeled as relation Class1 RelationACT Class2 duration modeled as method Class methodact1() methodact2() 16

17 ACT as State (-Transition) instantanious modeled as state-(transition) Game On- Going Win Game Won 17

18 ACT as Relation eternal Help Desk Operator modeled as relation uses 18

19 ACT as Method duration modeled as method move() attack() Chesspiece 19

20 Conclusion Automatic modeling improved through more precise (more compact) modeling Our approach moves software process development control to earlier stages Lessons learned Ontology-coverage has blind spots Ontology has not been improved Could not distinguish: I eat fish vs I am eating fish : Determination vs Action 20

21 Future Work Annotation Working on tool for annotation support (Eclipse PlugIn) Evaluation Using other specifications then FIDE Comparing RCyc ontology to other ontologies Pre-Processing Skip non-functional aspects from specifications Find and omit Why? -clauses 21

22 Questions? 22

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