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

More documents

Recommendations

Info

112 CHAPTER 7. EXEMPLAR STUDIES AND APPLICATION SYSTEM Table 7.1: OWL definition of the SCOR ontology OWL Ontology Class Subsumption OWL Properties Property Range Relation owl:subClassOf hasInput multiple SCOR_MGMT_PROCESS Activity SCOR_INPUT_OUTPUT hasOutput multiple SCOR_INPUT_OUTPUT precedes multiple SCOR_MGMT_PROCESS isPrecededBy multiple SCOR_MGMT_PROCESS owl:subClassOf isInputTo multiple SCOR_INPUT_OUTPUT Artifact SCOR_MGMT_PROCESS isOutputOf multiple SCOR_MGMT_PROCESS has_state (data property inherited from Artifact SCOR_ORGANIZATIONAL owl:subClassOf Actor-role has sub-Class has_parts multiple Goal Hard Goal, Goal Soft Goal part_of multiple Goal targetActivity multiple Activity targetArtifact multiple Artifact targetRole multiple Actor-role targetConstraint (data property) Each SCOR_MGMT_PROCESS has object properties hasInput and hasOutput to specify inputs and outputs of each process element. The object properties precedes and isPrecededBy indicate logic flows between the process elements. Inversely, SCOR_INPUT_OUTPUT has the object properties isInputTo and isOutputOf to relate itself to SCOR_MGMT_PROCESS. A data property has_state is defined as a property of Artifact. This property can be used associated with isInputTo and isOutputOf properties to indicate that a same artifact with different states represents different inputs/outputs. Goal ontology concepts are organized into Hard and Soft Goals. Soft goals are further organized into some general soft goal categories. We integrate the domain ontology and goal ontology into one OWL file, because they are both derived from SCOR descriptions. According to the semantic representation of the goal ontology in Chapter 5, the values of the object properties targetActivity, targetArtifact, and targetRole are from the domain ontology Activity, Artifact and Actor-role respectively. TargetConstraint is modeled as a data property since there is no corresponding ontological definition in the domain ontology. A pair of inverse properties has_parts and part_of are defined for a goal concept to represent the semantics of goal decomposition. An OWL model of the SCOR ontology is presented in Table 7.1. The identified domain and goal concepts from the SCOR specifications are modeled as sub-Classes of the above categories. Values of the properties for a SCOR Class are specified through the OWL restrictions. As examples, Figure 7.11, Figure 7.12, Figure 7.13 and Figure 7.14 illustrate the sub-Classes of the SCOR_MGMT_PROCESS, SCOR_INPUT_OUTPUT, Hard Goal and Soft Goal in the Protégé-OWL editor.
7.4. ANNOTATION OF PROCESS MODELS WITH PRO-SEAT 113 Figure 7.11: SCOR process element S1.2 Receive Product in Protégé-OWL editor Figure 7.12: SCOR input/output Sourced Product On Order in Protégé-OWL editor 7.4 Annotation of Process Models with Pro-SEAT In this section, we will describe the annotation of PM A , PM B1 and PM B2 with the Pro- SEAT annotation tool. The annotation follows our semantic annotation framework and approach, including profile annotation, meta-model annotation, model annotation and goal annotation. 7.4.1 Profile annotation The descriptive and technical information about models in the profiles are important in this scenario. Title and description of models may help to identify the model content in general. The title of PM A is "sales logistics process" and the main processing is depicted in the description. The titles of PM B1 and PM B2 are "stock logistics process" and "delivery logistics process" respectively. In this scenario, the three models are classified under the same category "Business and Economy" [211] and domain is "SCO
Page 1 and 2:
Yun Lin Semantic Annotation for Pro
Page 3:
To MY BELOVED HUSBAND HAO DING AND
Page 6 and 7:
ii The proposed approach has been i
Page 8 and 9:
iv CONTENTS 3 State of the Art 31 3
Page 10 and 11:
vi CONTENTS 7.5.2 Semantic reasonin
Page 12 and 13:
viii CONTENTS H PSAM Annotation Res
Page 14 and 15:
x LIST OF FIGURES 6.1 System module
Page 16 and 17:
xii LIST OF FIGURES
Page 18 and 19:
xiv LIST OF TABLES
Page 20 and 21:
xvi PREFACE My tremendous gratitude
Page 23 and 24:
Chapter 1 Introduction Business pro
Page 25 and 26:
1.2. PROBLEM STATEMENT AND RESEARCH
Page 27 and 28:
1.4. APPROACH AND SCOPE 7 1.4.1 Sem
Page 29 and 30:
1.6. MAJOR CONTRIBUTIONS 9 1.6 Majo
Page 31 and 32:
Chapter 2 Problem Setting In this c
Page 33 and 34:
Figure 2.1: Zachman Enterprise Arch
Page 35 and 36:
2.2. MODELING BASIS 15 meta-model o
Page 37 and 38:
2.3. INFORMATION SYSTEMS AND SEMANT
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
2.4. SEMANTIC INTEROPERABILITY 23 3
Page 45 and 46:
2.5. BUSINESS PROCESS MODEL 25 2.5
Page 47 and 48:
2.6. PROCESS KNOWLEDGE MANAGEMENT 2
Page 49 and 50:
2.7. SUMMARY 29 • Creation or imp
Page 51 and 52:
Chapter 3 State of the Art This cha
Page 53 and 54:
3.1. PROCESS MODELING LANGUAGES 33
Page 55 and 56:
3.1. PROCESS MODELING LANGUAGES 35
Page 57 and 58:
Table 3.1: Modeling constructs of d
Page 59 and 60:
3.2. SEMANTIC INTEROPERABILITY AND
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Representation primitives Process P
Page 69 and 70:
3.3. GOAL MODELING 49 From the surv
Page 71 and 72:
3.4. SEMANTIC ANNOTATION METHODS AN
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
3.6. SUMMARY 59 In the goal modelin
Page 81: Part II Design and Application 61
Page 84 and 85: 64 CHAPTER 4. SEMANTIC ANNOTATION F
Page 104 and 105: 84 CHAPTER 5. GOAL ANNOTATION proce
Page 106 and 107: 86 CHAPTER 5. GOAL ANNOTATION in a
Page 108 and 109: 88 CHAPTER 5. GOAL ANNOTATION • i
Page 110 and 111: 90 CHAPTER 5. GOAL ANNOTATION 5.5 G
Page 112 and 113: 92 CHAPTER 5. GOAL ANNOTATION
Page 114 and 115: 94 CHAPTER 6. PRO-SEAT (PROCESS SEM
Page 120 and 121: 100 CHAPTER 6. PRO-SEAT (PROCESS SE
Page 122 and 123: 102 CHAPTER 6. PRO-SEAT (PROCESS SE
Page 124 and 125: 104 CHAPTER 7. EXEMPLAR STUDIES AND
Page 149 and 150: Chapter 8 Quality Evaluation of the
Page 151 and 152: 8.2. SETTING FOR THE QUALITY EVALUA
Page 153 and 154: 8.2. SETTING FOR THE QUALITY EVALUA
Page 155 and 156: 8.3. QUALITY ANALYSIS 135 to those
Page 157 and 158: 8.3. QUALITY ANALYSIS 137 annotatio
Page 159 and 160: 8.3. QUALITY ANALYSIS 139 • G1 -
Page 161 and 162: 8.5. SUMMARY 141 3. Semantic annota
Page 163 and 164: Chapter 9 Validation of Applicabili
Page 165 and 166: 9.1. VALIDATION DESIGN 145 - RE3.3
Page 167 and 168: 9.2. SWRL FORMULATION 147 Table 9.1
Page 169 and 170: 9.2. SWRL FORMULATION 149 RE3.2 RE3
Page 171 and 172: 9.3. APPLICABILITY VALIDATION IN AN
Page 179 and 180: 9.4. DISCUSSION ON RESULTS OF THE V
Page 181 and 182: 9.4. DISCUSSION ON RESULTS OF THE V
Page 183:
Part IV Synopsis 163
Page 186 and 187:
166 CHAPTER 10. CONCLUSIONS AND FUT
Page 188 and 189:
Page 190 and 191:
Page 193 and 194:
Appendix A BPMN This chapter presen
Page 195 and 196:
A.3. CONNECTING OBJECTS 175 Figure
Page 197 and 198:
A.6. BPMN META-MODEL TREE IN METIS
Page 199 and 200:
Appendix B EEML 2005 The language v
Page 201 and 202:
B.2. RECOURSES MODELING DOMAIN 181
Page 203 and 204:
Appendix C SCOR SCOR — Supply Cha
Page 205 and 206:
C.2. LEVEL 2 TOOLKIT 185 Level 1 Me
Page 207 and 208:
C.3. LEVEL 3 PROCESS ELEMENTS 187 F
Page 209 and 210:
Appendix D Algorithm for Semi-Autom
Page 211 and 212:
191 Algorithm 1 Goal Annotation Alg
Page 213 and 214:
Algorithm 4 Goal Annotation Algorit
Page 215 and 216:
195 Algorithm 6 Goal Annotation Alg
Page 217 and 218:
Appendix E GUI of Pro-SEAT The prop
Page 219 and 220:
199 Figure E.3: Mapping meta-model
Page 221 and 222:
201 Figure E.5: Goal annotation UI
Page 223 and 224:
Appendix F Analysis of the Annotati
Page 225 and 226:
F.2. INTEGRATION APPLICATION BASED
Page 227 and 228:
F.2. INTEGRATION APPLICATION BASED
Page 229 and 230:
Appendix G Schema of PSAM and SWRL
Page 231 and 232:
G.1. PSAM IN OWL 211
Page 233 and 234:
G.2. RULE DEFINITIONS IN SWRL 213
Page 235 and 236:
G.2. RULE DEFINITIONS IN SWRL 215
Page 237 and 238:
Appendix H Annotation Results in Ex
Page 239 and 240:
H.2. ANNOTATION OF PM B1 219 xmlns:
Page 241 and 242:
H.3. ANNOTATION OF PM B2 221 Av
Page 243 and 244:
Bibliography [1] ALTOVA. What is th
Page 245 and 246:
BIBLIOGRAPHY 225 [26] Dov Dori. Why
Page 247 and 248:
BIBLIOGRAPHY 227 [52] Ian Horrocks.
Page 249 and 250:
BIBLIOGRAPHY 229 [78] John Krogstie
Page 251 and 252:
BIBLIOGRAPHY 231 [102] Diana Maynar
Page 253 and 254:
BIBLIOGRAPHY 233 [129] OASIS Cover
Page 255 and 256:
BIBLIOGRAPHY 235 [157] Colette Roll
Page 257 and 258:
BIBLIOGRAPHY 237 [185] Systems Thin
Page 259:
BIBLIOGRAPHY 239 [214] John.A. Zach
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?