Semantic Annotation for Process Models: - Department of Computer ...

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98 CHAPTER 6. PRO-SEAT (PROCESS SEMANTIC ANNOTATION TOOL) Figure 6.6: Structure of entities in the model annotation Figure 6.7: Metis model structure Figure 6.6 displays the structure of entities in the model annotation, in which a model file is the annotation target. The model file is composed of model constructs. The ontological concepts defined in a domain ontology are referenced or semantically mapped to model elements in the model annotation. The Metis model element is one kind of model element and its members include ’Object’ and ’Relation’ — two abstract types in the meta-level definitions of the Metis model (Figure 6.7). In Figure 6.8 we can see that the structure of entities in the goal annotation analogous with the model annotation. One distinct difference is that an intermediate granularity of the model — model fragment is between a model file and model elements. The goal annotation can link a goal ontology concept to a model fragment that comprises several model elements. In terms of the process model annotation, the process model fragments are composed of a set of activities (Figure 6.9).
6.3. GOAL ANNOTATION ALGORITHM 99 Figure 6.8: Structure of entities in the goal annotation 6.3 Goal Annotation Algorithm In the current version of Pro-SEAT, there are no particular algorithms to implement the manual procedures of profile, meta-model and model annotations. However, based on the formal definitions of goal annotation (see Chapter 5), we design algorithms to semi-automate the goal annotation procedure. A semi-automatic goal annotation is implemented through the algorithms in the following cases. • A. Match a target activity (av ′ ) of a goal (g) in the goal ontology (G) with the Activity (av) in a PSAM model. • B. Match a target artifact (a f ′ ) of a goal (g) in the goal ontology (G) with the Artifact (a f ) and Output (o) in a PSAM model. • C. Match a target role (ar ′ ) of a goal (g) in the goal ontology (G) with the Act-role (ar) in a PSAM model. • D. Match a target constraint (c ′ ) of a goal (g) in the goal ontology (G) with the Precondition (pre), Postcondition (post), and Exception (e) in a PSAM model. To automate the matching, we exploit semantic mappings through both ontology comparison and string match between ontology references and model elements. To rank mapping results, weights are assigned to the different ways of mapping (σ is for a weight of ontology comparison. τ is for a weight of string match). In ontology comparison, three different semantic relationships applied in model annotation are taken into account in the assignment of weights. The synonym ("same_as") relationship is given the highest weight for a complete match (σ = 1), and the hypernym ("kind_of")
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Yun Lin Semantic Annotation for Pro
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To MY BELOVED HUSBAND HAO DING AND
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ii The proposed approach has been i
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iv CONTENTS 3 State of the Art 31 3
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vi CONTENTS 7.5.2 Semantic reasonin
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viii CONTENTS H PSAM Annotation Res
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x LIST OF FIGURES 6.1 System module
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xii LIST OF FIGURES
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xiv LIST OF TABLES
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xvi PREFACE My tremendous gratitude
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Chapter 1 Introduction Business pro
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1.2. PROBLEM STATEMENT AND RESEARCH
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1.4. APPROACH AND SCOPE 7 1.4.1 Sem
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1.6. MAJOR CONTRIBUTIONS 9 1.6 Majo
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Chapter 2 Problem Setting In this c
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Figure 2.1: Zachman Enterprise Arch
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2.2. MODELING BASIS 15 meta-model o
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2.3. INFORMATION SYSTEMS AND SEMANT
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2.4. SEMANTIC INTEROPERABILITY 23 3
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2.5. BUSINESS PROCESS MODEL 25 2.5
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2.6. PROCESS KNOWLEDGE MANAGEMENT 2
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2.7. SUMMARY 29 • Creation or imp
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Chapter 3 State of the Art This cha
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3.1. PROCESS MODELING LANGUAGES 33
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3.1. PROCESS MODELING LANGUAGES 35
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Table 3.1: Modeling constructs of d
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3.2. SEMANTIC INTEROPERABILITY AND
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Page 67 and 68: Representation primitives Process P
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Page 81: Part II Design and Application 61
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9.2. SWRL FORMULATION 149 RE3.2 RE3
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9.3. APPLICABILITY VALIDATION IN AN
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9.4. DISCUSSION ON RESULTS OF THE V
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9.4. DISCUSSION ON RESULTS OF THE V
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Part IV Synopsis 163
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166 CHAPTER 10. CONCLUSIONS AND FUT
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Appendix A BPMN This chapter presen
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A.3. CONNECTING OBJECTS 175 Figure
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A.6. BPMN META-MODEL TREE IN METIS
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Appendix B EEML 2005 The language v
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B.2. RECOURSES MODELING DOMAIN 181
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Appendix C SCOR SCOR — Supply Cha
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C.2. LEVEL 2 TOOLKIT 185 Level 1 Me
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C.3. LEVEL 3 PROCESS ELEMENTS 187 F
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Appendix D Algorithm for Semi-Autom
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191 Algorithm 1 Goal Annotation Alg
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Algorithm 4 Goal Annotation Algorit
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195 Algorithm 6 Goal Annotation Alg
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Appendix E GUI of Pro-SEAT The prop
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199 Figure E.3: Mapping meta-model
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201 Figure E.5: Goal annotation UI
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Appendix F Analysis of the Annotati
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F.2. INTEGRATION APPLICATION BASED
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F.2. INTEGRATION APPLICATION BASED
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Appendix G Schema of PSAM and SWRL
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G.1. PSAM IN OWL 211
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G.2. RULE DEFINITIONS IN SWRL 213
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G.2. RULE DEFINITIONS IN SWRL 215
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Appendix H Annotation Results in Ex
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H.2. ANNOTATION OF PM B1 219 xmlns:
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H.3. ANNOTATION OF PM B2 221 Av
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Bibliography [1] ALTOVA. What is th
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BIBLIOGRAPHY 225 [26] Dov Dori. Why
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BIBLIOGRAPHY 227 [52] Ian Horrocks.
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BIBLIOGRAPHY 229 [78] John Krogstie
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BIBLIOGRAPHY 231 [102] Diana Maynar
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BIBLIOGRAPHY 233 [129] OASIS Cover
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BIBLIOGRAPHY 235 [157] Colette Roll
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BIBLIOGRAPHY 237 [185] Systems Thin
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BIBLIOGRAPHY 239 [214] John.A. Zach
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