Semantic e-Science (eBook)

Huajun Chen, Kei-Hoi Cheung, Yimin Wang (Herausgeber)

eBook Download: PDF

2010 | 2010
VII, 352 Seiten
Springer US (Verlag)
978-1-4419-5908-9 (ISBN)

Infos

The Semantic Web has been a very important development in how knowledge is disseminated and manipulated on the Web, but it has been of particular importance to the flow of scientific knowledge, and will continue to shape how data is stored and accessed in a broad range of disciplines, including life sciences, earth science, materials science, and the social sciences. After first presenting papers on the foundations of semantic e-science, including papers on scientific knowledge acquisition, data integration, and workflow, this volume looks at the state of the art in each of the above-mentioned disciplines, presenting research on semantic web applications in the life, earth, materials, and social sciences. Drawing papers from three semantic web workshops, as well as papers from several invited contributors, this volume illustrates how far semantic web applications have come in helping to manage scientific information flow.

Huajun Chen received his B.S. from the Department of Biochemical Engineering, and Ph.D. from the College of Computer Science, both from Zhejiang University. At present, he serves as an associate professor in the college of computer science at Zheijiang University and was a visiting researcher at the school of computer science, Carnegie Mellon University. He is currently working for the China 973 'Semantic Grid' initiative and the leader of the e-Science DartGrid semantic grid project.

Yimin Wang is an associate information consultant in Lilly Singapore Centre for Drug Discovery. He is currently leading projects related to Semantic Web R&D in the division of Integrative Computational Science to support drug discovery research. Before joining Lilly Singapore, he was a research associate at the University of Karlsruhe, Institute of Applied Informatics and Formal Description Methods (AIFB). He received his MS in 2005 after studying Advanced Computer Science in the Medical Informatics Group at University of Manchester, supervised by Prof. Alan Rector.

Dr. Kei Cheung is an Associate Professor at the Yale Center for Medical Informatics. He received his PhD degree in Computer Science from the University of Connecticut. Since his PhD graduation, Dr. Cheung has been a faculty member at the Yale University School of Medicine. Dr. Cheung has a joint appointment with the Computer Science Department and Genetics Department at Yale. Dr. Cheung's primary research interest lies in the area of bioinformatics database and tool integration. Recently, he has embarked on the exploration of Semantic Web in the context of Life Sciences (including Neuroscience) data and tool integration. Dr. Cheung edited Semantic Web: Revolutionizing Knowledge Discovery in the Life Sciences (Springer) and served as the chair of the First International Workshop on Health Care and Life Sciences Data Integration for the Semantic Web, which was held cooperatively with the WWW2007 conference. He was he Guest Editor of the Special Issue: 'Semantic BioMed Mashup', Journal of Biomedical Informatics. Dr. Cheung is also an invited expert to the Semantic Web Health Care and Life Science Interest Group launched by the World Wide Web Consortium.

Inhalt

Preface 4
Contents 8
Contributors 9
About the Editors 12
1 Supporting e-Science Using Semantic Web Technologies The Semantic Grid 13
1.1 Introduction 13
1.2 The e-Science Vision 14
1.3 Semantic Grid: Service-Oriented Science 15
1.4 Semantic Web Essentials 17
1.5 Achieving the e-Science Vision 20
1.5.1 Resource Description, Discovery, and Use 21
1.5.2 Process Description and Enactment 22
1.5.3 Autonomic Behaviour 22
1.5.4 Security and Trust 23
1.5.5 Annotation 24
1.5.6 Information Integration 24
1.5.7 Communities 25
1.6 A Semantic Grid Approach to In Silico Bioinformatics 25
1.7 A Semantic Datagrid for Chemistry 27
1.8 Architectures for the Semantic Grid 31
1.8.1 S-OGSA -- A Reference Architecture for the Semantic Grid 31
1.8.2 The Service-Oriented Knowledge Utility 34
1.8.3 The Semantic Grid Community 35
1.9 Moving Forward 36
1.10 Summary 37
References 38
2 Semantic Disclosure in an e-Science Environment 41
2.1 Introduction 41
2.1.1 Semantic Disclosure 41
2.1.2 The Semantic Web 42
2.1.3 Making Sense of the Digital Deluge 43
2.1.4 Data Integration 44
2.1.5 W3C Semantic Web for Health Care and Life Sciences Interest Group 44
2.1.6 Semantic Architecture 45
2.1.7 The Virtual Laboratory for e-Science Project 46
2.2 The AIDA Toolkit 47
2.2.1 Storage -- The Metadata Storage Module 48
2.2.2 Learning -- The Machine Learning Module 50
2.2.3 Search -- The Information Retrieval Module 51
2.3 Applications of Adaptive Information Disclosure 52
2.3.1 Food Informatics -- Adaptive Information Disclosure Collaboration 52
2.3.2 A Metadata Management Approach to fMRI Data 54
2.3.3 Semantic Disclosure in Support of Biological Experimentation 57
2.3.3.1 Application Case 1: Semantic Disclosure of Human Genome Data 57
2.3.3.2 Application Case 2: Semantic Disclosure of Biological Knowledge Trapped in Literature 61
2.3.3.3 An e-Science Approach for Extracting Knowledge from Text 62
2.3.3.4 Conclusion 71
2.4 Discussion 72
References 76
3 A Smart e-Science Cyberinfrastructure for Cross-Disciplinary Scientific Collaborations 78
3.1 Introduction 78
3.2 Background 81
3.2.1 Challenges for Smart e-Science 81
3.2.2 Enabling Technologies for Smart e-Science 82
3.2.2.1 Grid Computing 82
3.2.2.2 Service-Oriented Architecture and Web Services 82
3.2.2.3 Semantic Web and Ontology 83
3.2.3 Contemporary Efforts in e-Science Cyberinfrastructure 84
3.2.4 Case Study: Integration of Scientific Infrastructures 85
3.3 The Smart e-Science Framework 87
3.3.1 The e-Science Ontology 88
3.3.2 Grid-Based Service Orientation 93
3.3.3 Service Interface 93
3.3.4 Semantic Interface 94
3.3.5 The Proxy 94
3.3.6 Knowledgebase Design 95
3.3.7 Data Exchange 95
3.4 Implementation 97
3.4.1 Ontology Design 98
3.4.2 Server-Side Service Implementation 99
3.4.3 Client-Side Service Implementation 100
3.4.4 Fundamental and Composed Services 102
3.4.4.1 Users and Application Services 102
3.4.4.2 Sensor Resources Proxy Services 103
3.4.4.3 Compute/Data Resources Services 104
3.4.5 Conceptual and Semantic Schema 105
3.5 Conclusions 105
References 106
4 Developing Ontologies within Decentralised Settings 109
4.1 Introduction 109
4.1.1 Decentralised Communities 110
4.1.2 Community-Driven Ontology Engineering 111
4.1.3 Upper Level Ontologies 113
4.1.4 Dynamic Ontologies 113
4.1.5 The Melting Point: A Methodology for Distributed Community-Driven Ontology Engineering 114
4.2 Review of Current Methodologies 115
4.2.1 Criteria for Review 115
4.2.2 Finding the Melting Point 119
4.3 The Melting Point Methodology 123
4.3.1 Definition of Terminology 123
4.3.2 Management Processes 125
4.3.3 Documentation Processes 126
4.3.4 Development-Oriented Processes 127
4.3.5 Evaluation 131
4.4 Discussion 132
4.4.1 Melting Point Evaluated 132
4.4.2 IEEE Standards Compliance 133
4.4.3 Quality Assurance 133
4.4.4 Activities Become Interrelated 134
4.4.5 Recommended Life Cycle: Incremental Evolutionary Spiral 135
4.5 Conclusions 135
A. Appendix: Review of Methodologies 137
A.1 The Enterprise Methodology 137
A.2 The TOVE Methodology 138
A.3 The Bernaras Methodology 139
A.4 The METHONTOLOGY Methodology 140
A.5 The SENSUS Methodology 141
A.6 DILIGENT 142
A.7 The GM Methodology 143
A.8 The iCapturer Methodology 143
A.9 NeOn Methodology 145
References 145
5 Semantic Technologies for Searching in e-Science Grids 150
5.1 Introduction 150
5.1.1 e-Science or Scientific Cyber-Infrastructure 150
5.1.2 Scientific Cyber-Infrastructure Planning and Designing Challenges: Scope of Discussion 151
5.1.3 Role of Search and Semantic Technologies 153
5.1.4 Chapter Overview 154
5.2 Scientific Cyber-Infrastructure: Functional Requirements 154
5.2.1 Business Processes in Research 155
5.2.2 Cyber-Infrastructure Functional Blocks and Enabling Technologies 156
5.3 Search Technology for Cyber-Infrastructures 160
5.3.1 Search Basics 160
5.3.2 Search and Retrieval Performance Metrics 162
5.3.3 Existing Meaning-Based Search Techniques 163
5.3.4 Intention-Based Web Searching 163
5.4 Semantic Technologies: Requirements and Literature Survey 164
5.4.1 Crucial Semantic Technologies 164
5.4.2 Requirements for Semantic Technologies 164
5.4.3 A Study of Meaning in Human Cognition and Language 165
5.4.4 Meaning Representation in Computers: Existing Works 169
5.5 Proposed Semantic Technologies for Cyber-Infrastructure 175
5.5.1 Overview 176
5.5.2 Concept Tree Representation 177
5.5.3 Required Algebra 179
5.5.4 Tensor Representation of Concept Tree 182
5.5.5 Bloom Filter Basics 185
5.5.6 Generation of Bloom Filter-Based Descriptor Data Structure 186
5.5.7 Descriptor Comparison Algorithm 187
5.5.8 Extensions for Incorporating Synonym and Hypernyms 188
5.6 Discussions 191
5.7 Conclusion 192
References 193
6 BSIS: An Experiment in Automating Bioinformatics Tasks Through Intelligent Workflow Construction 197
6.1 Introduction 197
6.2 Related Work 198
6.2.1 Custom Scripts 198
6.2.2 Domain-Specific Programming Environments 199
6.2.3 Integrated Analysis Environments 199
6.2.4 Workflow Systems 200
6.3 An Overview of the BioService Integration System 201
6.3.1 The Web Service Infrastructure 201
6.3.2 The Workflow Language 202
6.3.3 The Planner 202
6.3.4 The Executor 202
6.4 The BSIS Web Service Infrastructure 202
6.4.1 Domain Ontologies 203
6.4.1.1 Service Ontology 203
6.4.1.2 Data Ontology 204
6.4.2 Services Description 205
6.4.3 Services Registry 208
6.5 Workflow Language 209
6.5.1 Entity Nodes 209
6.5.1.1 Service nodes 209
6.5.1.2 Data Nodes 211
6.5.1.3 Control Nodes 211
6.5.1.4 Operator Nodes 213
6.5.2 Connectors 213
6.5.3 Development of Sample Workflows 214
6.5.4 Workflow Language Formalization 216
6.5.5 A Prototype Implementation of the Workflow Language 217
6.6 The Planner 218
6.6.1 Objectives of the Planner 218
6.6.2 Service Mapping 219
6.6.3 Quality of Service 222
6.6.4 Data Binding 223
6.6.5 Data Conversion 225
6.6.5.1 Guided Search 226
6.6.5.2 Blind Search 226
6.6.6 Planning the Workflow 228
6.6.7 Planning with an External Planner 229
6.6.7.1 Situation Calculus 229
6.6.7.2 C-Golog 230
6.6.7.3 Workflows as Golog Programs 231
6.7 Executor 234
6.7.1 Execution Framework 234
6.7.2 Extension Mechanism 236
6.7.2.1 Web Service Providers 236
6.7.2.2 Custom Operator 237
6.7.3 BioPerl Modules 238
6.8 Case Studies and Optimizations 238
6.8.1 Some Case Studies 238
6.8.2 Optimization 240
6.9 Conclusion and Future Work 242
References 244
7 Near-Miss Detection in Nursing: Rules and Semantics 247
7.1 Introduction 247
7.2 Nursing Domain and Near Miss 249
7.2.1 Nursing 250
7.2.2 Bone Marrow Transplantation 251
7.2.3 Bone Marrow Transplantation Nursing 252
7.2.4 Human Factor and Near Miss 253
7.2.5 Service, SLA, Plans, Rules, Patterns 254
7.2.6 Real Life Event Ordering 255
7.2.7 Behaviour Formalisation 258
7.3 Technologies for Near Miss in Nursing 259
7.3.1 Identification 259
7.3.2 Ubiquity 262
7.3.3 Adaptivity 263
7.3.4 Sensing and Multi-sensor Fusion 264
7.3.5 Presence and Context 266
7.3.6 Uncertainty and Rule-Based Systems 267
7.3.7 Ontology Engineering in Bone Marrow Transplantation 269
7.4 Knowledge and Semantics 271
7.4.1 Nursing Process Modelling 273
7.4.2 Application 277
7.5 Implementation 280
7.5.1 Architecture 280
7.5.2 Selection of Components 282
7.5.3 Intelligent Layer 285
7.5.4 Feedback 286
7.5.5 Correlations 287
7.6 Conclusions 291
References 292
8 Toward Autonomous Mining of the Sensor Web 296
8.1 Introduction 296
8.2 Sensor Web with Semantics 297
8.3 Semantically Enabled Earth Science Model Service 299
8.4 Semantic Web Service-Based Approach 301
8.5 Process of Sensor Web Mining 303
8.6 Ontology-Based Knowledge Base 304
8.7 OGC Catalogue Service for Web (CS/W) with Semantic Augmentations 307
8.8 Geospatial Web Services 308
8.8.1 Data Services 308
8.8.2 Data Fusion Services 309
8.8.3 Earth Science Model Services 309
8.9 Mining Planner 310
8.10 BPEL Engine 311
8.11 Conclusions 312
References 313
9 Towards Knowledge-Based Life Science PublicationRepositories 315
9.1 Introduction 315
9.1.1 Motivation 316
9.1.2 State-of-the-Art Overview 317
9.1.3 Main Contributions and Structure of the Chapter 318
9.2 Overview of Our Approach 319
9.3 Emergent Knowledge Processing Framework 320
9.3.1 Empirical Knowledge Representation 320
9.3.2 Inference Services 326
9.3.3 Notes on the Theoretical Principles' Implementation 331
9.4 Processing the Publication Data 332
9.4.1 Data 332
9.4.2 Method 334
9.5 Using CORAAL 338
9.6 Preliminary Tests with Domain Experts 342
9.7 Related Work 345
9.7.1 Similar Approaches to Emergent Knowledge Processing 345
9.7.2 Conformance to the Semantic Web Standards 346
9.8 Conclusion and Future Work 347
References 349

Produkt-Details

• Erscheint lt. Verlag: 2.9.2010
• Reihe/Serie: Annals of Information Systems
• Reihe/Serie: Annals of Information Systems
• Zusatzinfo: VII, 352 p. 127 illus.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Wirtschaft ► Allgemeines / Lexika
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Wirtschaftsinformatik
• Schlagworte: E-Science • Informatics • knowledge management • linear optimization • Ontology • semantic web • service-oriented computing • Workflow
• ISBN-10: 1-4419-5908-4 / 1441959084
• ISBN-13: 978-1-4419-5908-9 / 9781441959089