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

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120 CHAPTER 7. EXEMPLAR STUDIES AND APPLICATION SYSTEM Table 7.5: Part of semantic annotation results of PM B2 PSAM Activity Semantic Annotation Goal Annotation SCOR Goal Ontology Instance Relations Consolidate orders same_as D1.4 Consolidate Orders achieves Order is Processed; Order is Consolidated positively_satisfies Reduce Order Processing Costs; Reduce Order Processing Time Check stock phase_of D1.3 Reserve Inventory achieves Order is Validated and Determine Delivery Date positively_satisfies Raise Order Quantity Fillrate Correct orders phase_of D1.2 Receive Enter and Validate Order achieves Order is Validated; Order is Processed positively_satisfies Ensure Full Delivery negatively_satisfies Reduce Order Entry and Maintenance Costs; Reduce Order Processing Time Credit control phase_of D1.2 Receive Enter and achieves Order is Validate Generate protocol delivery Validate Order kind_of ED.3 Manage Deliver Information kind_of ED.3 Manage Deliver Information positively_satisfies Reduce Order Entry and Maintenance Costs negatively_satisfies Reduce Order Processing Time achieves Delivery Terms are Generated positively_satisfies Improve Deliver to Customer Delivery to Date Performance; Improve Deliver to Customer On Time Delivery Performance Issue invoice phase_of D1.15 Invoice achieves Invoice to Customer is Issued negatively_satisfies Reduce Invoice to Customer Processing Time; Reduce Invoice to Customer Processing Costs; Process Invoice Without Error Send items kind_of D1.12 Ship Product achieves End Items are Delivered Deliver items to franchisees Deliver items to shops phase_of D1 Deliver Stocked Product phase_of D4 Deliver Retail Product achieves achieves Invoice to Customer is Issued; End Items are Delivered End Items are Delivered positively_satisfies Reduce Order Shipment Time
7.5. PROCESS KNOWLEDGE MANAGEMENT SYSTEM 121 7.5.1 System architecture The architecture of the system for process knowledge management is shown in Figure 7.15. Since the process knowledge management system is based on the semantic annotations, we include the annotation client tool in the system architecture. In the context of this thesis, the annotation client tool is Pro-SEAT. Annotators works with the annotation client too to annotate the original process knowledge resources. Annotators in this scenario should be model experts and domain experts to assure the annotation quality 1 . The original process knowledge resources — models and meta-models in XML files and XML schema files are stored in the local process model repository. As references, ontologies are essential in our approach. Ontologies are stored on an ontology server. When annotators employ the annotation with the client tool, the selected ontologies must be loaded from the ontology server. The annotator should select appropriate domain ontologies and goal ontologies according to the ontology categories. The GPO ontology is only applied for the meta-model annotation. Completing the annotation, annotators submit the annotated process models and/or the annotated meta-models to process knowledge repository server. It is allowed to modify the annotated models stored on the knowledge repository server but the authorization and the authentication are required (which we do not consider in this approach). Knowledge users might be different from annotators. They do not know about the original process models but the system will assist them to access the desired process models. There are three applications for knowledge users, i.e. process knowledge query, discovery and navigation. Those applications are implemented by client user interfaces and application servers. Through the user interface, users can edit the queries or provide business goals. Queries can be keyword-based or ontology-based. For the ontology-based query, users can browse domain ontologies from the ontology repository and use ontology concepts to form a query. Goal ontologies can be applied by users to set goals for the knowledge discovery. When the application server receives queries or goal requirements from the client, the matchmaker component and the reasoner component co-work with ontologies to find process models. The reasoner uses description logic inference technology on both the ontological query and the ontological annotation, because ontologies and annotations are all in OWL. Based on the inference, the matchmaker matches user’s query/goals with annotated models in the repository. The application server sends the search results to the client and the returned knowledge/models can be explored by the client. Users can navigate process knowledge/models from different views according to the process properties defined in PSAM. 7.5.2 Semantic reasoning The underlying logic basis of OWL is description logic, which can provide the inference services of the knowledge for semantic reasoning (w.r.t. an ontology O) [53]: • Consistency. Check if the knowledge is meaningful. (Is C is satisfiable w.r.t. O? i.e. C I ̸= ∅ in some model I of O?) 1 The quality of annotation affects the quality of knowledge.
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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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Representation primitives Process P
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3.3. GOAL MODELING 49 From the surv
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3.4. SEMANTIC ANNOTATION METHODS AN
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3.6. SUMMARY 59 In the goal modelin
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Part II Design and Application 61
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64 CHAPTER 4. SEMANTIC ANNOTATION F
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170 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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