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

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56 CHAPTER 3. STATE OF THE ART 3.4.8 ASTAR A* is the ATHENA semantic annotation system, which is partially supported by the ATHENA project [57]. A* is a Web application connected with ATHOS tool and with THEMIS tool — two tools are used to model the resources in RDF repositories. Ontology-based semantic annotation in this system is aimed at reconciling semantic heterogeneity among business and technical resources. The following semantic mismatch cases are identified in Table 3.4 [145]. A* allows four increasing levels of annotation, namely terminological, path, simple and full level annotations. • Terminological Annotation. By such annotation, terms are associated with a set of terms from the ontology. Usually naming mismatch, content mismatch and abstraction mismatch can be reconciled by terminology annotation. • Path Annotation. The structure of the schema and the ontology are compared. Complete paths, from the root element to the leaves, of the annotated schema are associated with a set of paths of the ontology. Such annotation can solve the semantic problems such as structural mismatch, subClass-Attribute mismatch, schema instance mismatch and content mismatch. • Simple annotation. At this level it is possible to specify the type of mismatch that each annotation intends to cover. Such information will be re-used for Attribute Granularity mismatch; Encoding mismatch; Precision mismatch, among others. • Full Semantic Annotation. It is a complex annotation including OWL expressions. 3.4.9 Discussion on the survey results of annotation tools and methods Most of annotation tools and methods in the survey are for unstructured Web textual resources, such as MnM, KIM, AeroDAML and OntoMat-Annotizer, because there have been many research activities and development in this area. For those unstructured information, the annotation is to structure and supply semantic descriptions for information. Information extraction is one of the techniques often applied for the automation of the semantic annotation of the textual resources. We have also surveyed the semantic annotation of structured information — WSDL information for the Web services discovery and composition, such as MWSAF and SAWSDL. Since WSDL is already structured, the annotation is to build the mapping between the structured information and the ontological concepts and relations. Some NLP techniques can also be employed to automate the mapping. Very few work has been completed on the semantic annotation of semi-structured information. Having the both characters of the unstructured and the structured, the semantic annotation of semi-structured information should be able to formalize the structure of information and also build the links between information and ontology. We has included the related work in the INTEROP project, which presents some initial ideas and surface results about the semantic annotation of enterprise models. Another related work — A* provides a tool to annotate models in order to develop future model transformations. The limitation of A* is that
3.4. SEMANTIC ANNOTATION METHODS AND TOOLS 57 Table 3.4: Lossless mismatches and examples Name Description Document Schema Reference Ontology Naming different labels for the request for quotation request for quotation same concept indicated as: RFQuote indicated as: RFQ Attribute the same information is decomposed sented as a single sition of hasPartCoun- Telephone is repre- Telephone is a compo- Granularity into a different number of attributes (or Code, hasPartLocal- string tryCode, hasPartAreasub-attributes) PhoneNumber Structuring different design structures in organizing the information SubClass- Attribute Schema- Instance an attribute, with a predefined value set, is represented by a set of subclasses, one for each value data holding schema information. In the DS example, the semantics of the second field (Name) depends on the value of the first field (Role). In the RO, the semantics is fully captured in the attribute names. Encoding different formats of data or units of measure (a Weight expressed in different unit of measure) Content different content denoted by the same concept - typically expressed by enumeration (the concept described by different enumeration items) Coverage presence/absence of information (a given information is considered in the RO, but not in DS) Precision the accuracy of information Abstraction level of specialisation/refinement of the information: generic vs detailed representation Buyer is directly linked to PurchaseOrder the type RawMaterial can be represented as an enumeration (’iron’, ’copper’) ContacInfo are represented as a pair: Role and Name. While Role is an enumeration = ’Director’, ’Secretary’, Name is a string Weight expressed in ounces PaymentTerms=(30days, 60days, 90days) preferredDeliveryDate is not considered in the DS Buyer is linked to the Parties concept and Parties is linked to PurchaseOrder ... or as two subclasses: iron subClassOf Raw- Material, copper sub- ClassOf RawMaterial ContacInfo has one field for each Role, which is indicated in the attribute name: DirectorName, SecretaryName, both strings Weight expressed in kilograms PaymentTerms=(30days, 45days) preferred delivery date is present in the RO Size of a pallet expressed Size of a pallet ex- by three integer pressed by a constant values: height, length, conventional value: width (small, medium, large) DeliveryTerms DeliveryTerms gets specialized into: NationalDeliveryTerms and InternationalDeliveryTerms
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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
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 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 67 and 68: Representation primitives Process P
Page 69 and 70: 3.3. GOAL MODELING 49 From the surv
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Page 79 and 80: 3.6. SUMMARY 59 In the goal modelin
Page 81: Part II Design and Application 61
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106 CHAPTER 7. EXEMPLAR STUDIES AND
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Chapter 8 Quality Evaluation of the
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8.2. SETTING FOR THE QUALITY EVALUA
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8.2. SETTING FOR THE QUALITY EVALUA
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8.3. QUALITY ANALYSIS 135 to those
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8.3. QUALITY ANALYSIS 137 annotatio
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8.3. QUALITY ANALYSIS 139 • G1 -
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8.5. SUMMARY 141 3. Semantic annota
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Chapter 9 Validation of Applicabili
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9.1. VALIDATION DESIGN 145 - RE3.3
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9.2. SWRL FORMULATION 147 Table 9.1
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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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