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

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122 CHAPTER 7. EXEMPLAR STUDIES AND APPLICATION SYSTEM Figure 7.15: Architecture of the process knowledge management system based on semantic annotation • Subsumption. Structure knowledge and computer a taxonomy. (Does C subsume D w.r.t. O? i.e. C I ⊑ D I in every model I of O?) • Equivalence. Check if two classes denote same set of instances. (Is C equivalent to D w.r.t. O? i.e. C I = D I in every model I of O?) • Instantiation. Check if individual is an instance of a class. (Is x an instance of C w.r.t. O? i.e. x I ∈ C I in every model I of O?) • Retrieval. Retrieve the set of individuals that instantiate a class and a property. – Retrieve the set of i s.t. i ∈ C w.r.t.O. – Retrieve the set of (x, y) s.t. (x, y) ∈ R w.r.t.O.
7.5. PROCESS KNOWLEDGE MANAGEMENT SYSTEM 123 Semantic reasoning can be useful in the ontology-based querying of process knowledge. The reasoning about the classes and properties of an ontology is often called as T-Box inference, whilst A-Box inference is associated with instances of the classes [53]. In the process knowledge management system, the knowledge model PSAM is the instance of the GPO ontology and the contents in PSAM are from domain ontology (e.g. the SCOR ontology in the exemplar studies). Therefore, A-Box inference can be employed when retrieving the process model fragments which are represented as the GPO instances in the PSAM model. Meanwhile, T-Box inference can be applied on reasoning the semantic relationships of the ontological concepts between query and domain ontology. 7.5.3 Simple walkthrough examples We use some annotation results from the exemplar studies to exemplify how the system architecture and semantic reasoning technique are applied in the application of process knowledge management. Three enterprise models have been annotated with GPO and SCOR ontologies. The annotated model fragments represented in PSAM are stored in the repository, which are to be retrieved, discovered and navigated by model users. In one example, the model user needs to look for some model fragments about the process which deal with "Bill" in both enterprise systems. The following steps must be taken (formalized in (i), (ii), and (iii)): 1. The search question must be translated as a query using PSAM "language" derived from GPO ontology and terminologies from SCOR ontology. • The process is defined as Activity and "Bill" is instance of Artifact according to the GPO ontology. • The term "Bill" in the user’s question is same as the concept "Bill" in the SCOR ontology. • The query submitted to the system is then interpreted as "Find the Activities which have Artifact Bill.", which is formalized in (i) with respect to the GPO and SCOR ontology. 2. The system analyzes and executes the query accompanied with reasoner. • The reasoner employs the T-Box inference to get all the equivalent concepts and all the sub-Classes of Bill in the SCOR ontology, which is formalized in (ii). In this case, Invoice is semantically equal to Bill, and Bill of Materials is a sub-Class of Bill. • The query is expanded by the three concepts, i.e. "Find the Activities which have Artifact Bill, Invoice or Bill of Materials". • According to the PSAM annotation, the model instances of Artifact can have the references of Bill, Invoice, and Bill of Materials through the semantic relationships such as same_as, kind_of and part_of. The query is hereby expanded based on the annotation information, which is formalized in (iii). A-Box inference is applied in (iii) to find out the model fragments (x, y) which are annotated as the instances of Activity and Artifact.
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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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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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