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136 CHAPTER 8. QUALITY EVALUATION OF THE METHOD concepts. For example, the meronym is represented by "phase_of" for Activity, "part_of" for Artifact and "member_of" forActor-role. 5. Tool appropriateness. PSAM is defined using a formal syntax and modeled in OWL. OWL is XML-syntactic compilable and it can be parsed by available commercial or non-profit parsers. The semantics of GPO and PSAM are also formally defined according to the OWL semantic definitions. The GPO concepts are modeled as owl:Class and the relations are modeled as owl:ObjectProperty and owl:DatatypeProperty. For our work, OWL DL is the basis of the annotation models. OWL DL was designed to support the existing Description Logic business segment and has desirable computational properties for reasoning systems [195]. The Protégé-OWL API is integrated in the annotation tool so that the syntax and the semantics of GPO and PSAM can be interpreted by the tool. 6. Organizational appropriateness. Due to the fair comprehensibility appropriateness and the application goals of this work, GPO and PSAM are theoretically easy to understand for audiences from different enterprise units. However, further empirical evidence is required to establish this claim. 8.3.2 Quality analysis of the annotation model instances A specific annotation model is an instance of the PSAM model that is transformed from the original process models and annotated with ontological concepts. The quality of GPO and PSAM will impact the quality of the PSAM models. The evaluation of the PSAM models concludes how the model quality relates to the usability of the annotation results. Physical Quality We first look at how the knowledge of the domain has been externalized by the annotation models. Such a goal could be achieved through the means of domain appropriateness, participant language knowledge appropriateness and knowledge externalizability appropriateness. As we have discussed in the quality analysis of those appropriateness above, the PSAM models can present most information about the process and functional perspective. The EEML and BPMN models have presented the logistics processing comprehensively due to their expressiveness. Based on the meta-model annotation results, the original models are transformed into the PSAM models. Ideally, the transformation should keep exactly the same information as the original models. It is obvious that more one-to-one mappings in the meta-model annotation, more knowledge represented in the modeling elements can be preserved after the transformation. In the exemplar studies, five one-to-one mappings are in the annotation of BPMN and three are in the annotation of EEML. In EEML, different resources are specified and they all could be mapped to the GPO concepts Artifact and Actor-role respectively. Thus, the PSAM models of PM A should have better physical quality than the ones of PM B . Fortunately, those specific resources are not much applied in the original EEML models PM B . That is, not much domain knowledge is lost because of the meta-model
8.3. QUALITY ANALYSIS 137 annotation in this case. However, annotating the relationships defined in the modeling languages is not supported by this approach. The relationships between the GPO concepts are defined in GPO so that knowledge in the PSAM models are interpreted according to the GPO definitions. Compared with the original models, the PSAM models have additional knowledge — the ontological knowledge, which is introduced during the model annotation and the goal annotation. In the exemplar studies, the domain ontology is related to the SCOR standard, while the goal ontology provides the knowledge about the objectives of process and also the links to the SCOR process metrics. In chapter 9, we discuss what useful knowledge can be externalized by the PSAM models. The PSAM models are then checked to determine if they are easily available and maintainable. The meta-model annotation result for each modeling language is stored in an XML file. Such results can be reused in generating the PSAM models by different models in the same modeling language. The generated PSAM models are OWL models, so that the PSAM models can be read and edited by any OWL editing tools. Model annotation and goal annotation is then made to the PSAM models. However, if there is any change of the meta-model annotation, the PSAM models based on the meta-model annotation result have to be re-generated. That means all the model and the goal annotation work made on the PSAM models will be lost. Empirical Quality There is no graphic notation for the annotation model. All the generated PSAM models are textual OWL files so that the readability of the model is poor without the tool support. The PSAM models are categories according to the GPO concepts. Since no graphic notation is used for the GPO concepts, the concepts such as "Activity", "Artifact", "Actor-role" are not distinct to the user. There is a limited number of categories since only eight GPO concepts are used in the exemplar studies. However, when the original model is large 2 , the list of the instances for each GPO concept is long. For example, 33 instances of Activity are listed for the annotation in PM A and only 14 instances of Activity in PM B2 . From the experience of using Pro-SEAT, it turns out that it is more difficult to find a desired instance in the PSAM model of PM A compared with the one of PM B2 . The instance is named with the model textual title and its model id from the original process models. When two instances have the same model title but different model id, mistakes will be made by picking the wrong instance because of a confusion with the title. Syntactic Quality As an OWL model with classes and instances, the PSAM models should comply the OWL syntax. Since the PSAM models are generated from the meta-model annotation result, the premise is that the syntax of GPO and the mappings have been validated. GPO is created by Protégé and the correctness of the syntax is checked by Protégé. The meta-model mapping rules have also been set to comply with OWL syntax. 2 The size of the model depends on the number of the model elements, i.e. the instances in the PSAM model.
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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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84 CHAPTER 5. GOAL ANNOTATION proce
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Page 161 and 162: 8.5. SUMMARY 141 3. Semantic annota
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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
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Page 193 and 194: Appendix A BPMN This chapter presen
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Page 201 and 202: B.2. RECOURSES MODELING DOMAIN 181
Page 203 and 204: Appendix C SCOR SCOR — Supply Cha
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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 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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