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

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52 CHAPTER 3. STATE OF THE ART providing unified structured metadata. Emerging semantic annotation of Web services is relating concepts in Web services to domain specific ontologies, such as MWSAF (METEOR-S Web Service Annotation Framework) [133]. Manual annotation is a tedious work. Semi-automatic and automatic annotation can facilitate this task. Some efforts on automatic and semi-automatic semantic annotation of textual documents have already done in [32] and [70]. Usually, the text extraction using some lexical analysis and grammatical functions is applied to achieve the semi-automatic annotation. AI technologies and semantic mapping are often used in Web services for the semi-automatic semantic annotation. 3.4.1 MnM MnM [184] is an annotation tool to support both automated and semi-automated semantic markup of Web pages. MnM integrates a Web browser with an ontology editor and provides open APIs to link to ontology servers and for integrating information extraction tools [193]. With the browser, users can have an overview of the knowledge models which are stored on the ontology server. A set of tags defined in the ontology are selected to annotate segments of text on web pages. MnM uses SGML/XML format tags and inserts them into the document. A machine learning technique is applied for information extraction in MnM. The manual tagged documents are treated as training corpus over which a learning algorithm is run to learn the extraction rules. Learning can result in a library of induced rules which can be used to extract information from texts. The extracted information is sent to the ontology server and fills predefined slots associated with an extraction template. The slots are the properties of a particular class defined in the ontology. MnM can handle multiple ontologies at the same time and allows inherited definitions to be displayed for ontology editing and browsing. Moreover, MnM can access ontology servers through APIs, and also access ontologies specified in a markup format, such as RDF and DAML+OIL [193]. 3.4.2 KIM (Knowledge & Information Management) KIM [127] is a knowledge and information management platform for automatic semantic annotation, indexing and retrieval of Web documents. The automatic semantic annotation in KIM can be seen as a classical named-entity recognition (NER) and annotation process. The NE (Named-Entity) type is specified through a reference to the KIMO (KIM Ontology) with the focus on named entity classes. KIMO is a lightweight top-level ontology including some basic distinctions between entity types (such as locations, agents, events, situations), real-world entity types of general importance (e.g. mettings, employment positions, commercial, government), and characteristic attributes and relations for featured entity types. The KIM KB (Knowledge Base) aims to provide quasi exhaustive coverage of the most important entities in the world. The entities stored in KB are instances with their proper classes and aliases. The entity descriptions in KB and the KIM ontology are stored in the same RDF(S) repository. Thus, KIM provides for each entity reference in the document (i) a link (URI) to the specific class in the ontology, and (ii) a link to the specific instance in the KB [136].
3.4. SEMANTIC ANNOTATION METHODS AND TOOLS 53 The dual mapping allows the information extraction process to be improved by providing disambiguation clues based on attributes and relations [153]. KIM uses GATE’s document management functionality and other generic NLP components such as Tokenizer, Part-of-Speech Tagger, and Sentence Splitter [136] in the information extraction processing. 3.4.3 AeroDAML AeroDAML [72] is a knowledge markup tool applying NLP (Natural Language Processing) techniques to automatically generate DAML annotations from Web pages. The natural language information extraction techniques are used to assign words and relationships to ontologies as instances of DAML classes and properties. A commercial information extraction product called AeroText is used in AeroDAML as the NLP component. There is a common knowledge base in AeroDAML which contains domain independent rules for extracting proper nouns and relations. DAML generation components are used to translate the extraction results into a RDF triple model by referencing an ontology. As a result, the RDF model is serialized to a DAML annotation. Two levels of default ontologies can be referenced: the lower level ontology from the common knowledge base and the top level ontology from the WordNet noun synset hierarchy. In addition, a custom ontology can also be input as the reference ontology in the annotation. 3.4.4 OntoMat-Annotizer OntoMat-Annotizer (OntoMat for short) is a component-based, ontology-driven annotation tool for Web documents. It is the implementation of CREAM (CREAting Metadata for the Semantic Web), a framework for an annotation environment. In the CREAM method, an ontology is represented by statements expressing definitions of DAML+OIL classes and properties. Based on those definitions, annotations are a set of instantiations of classes, attributes and relationships that attached to an HTML document. Annotations are described through the metadata which are derived from ontology definitions. Such metadata is stated as relational metadata because it contains relationship instances [49]. OntoMat supports both manual and semi-automatic annotation. The semi-automatic annotation approach is developed in S-CREAM (Semi-automatic CREAM) The semiautomatic metadata creation takes advantage of information extration techniques to propose annotations to metadata creators. A learnable information extraction component – Amilcare [16] is integrated in S-CREAM. 3.4.5 MWSAF (METEOR-S Web Service Annotation Framework) MWSAF [133] is a framework for semi-automatic annotation of Web service descriptions with relevant ontologies. MWSAF is developed under the METEOR-S project [187]. METEOR-S provides a mechanism to add data, functional, execution, and QoS (Quality of Service) semantics to WSDL files and a comprehensive framework for Semantic Web composition is provided in MWSCF [166].
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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
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
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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
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Page 53 and 54: 3.1. PROCESS MODELING LANGUAGES 33
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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
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102 CHAPTER 6. PRO-SEAT (PROCESS SE
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104 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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