Chen / Wang / Cheung Semantic e-Science

1. Auflage 2010
ISBN: 978-1-4419-5908-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 11, 352 Seiten

Reihe: Annals of Information Systems

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.

Autoren/Hrsg.

Chen, Huajun

Wang, Yimin

Cheung, Kei-Hoi

Chen, Jiaoyan

Inhaltsverzeichnis

1;Preface;4
2;Contents;8
3;Contributors;9
4;About the Editors;12
5;1 Supporting e-Science Using Semantic Web Technologies The Semantic Grid;13
5.1;1.1 Introduction;13
5.2;1.2 The e-Science Vision;14
5.3;1.3 Semantic Grid: Service-Oriented Science;15
5.4;1.4 Semantic Web Essentials;17
5.5;1.5 Achieving the e-Science Vision;20
5.5.1;1.5.1 Resource Description, Discovery, and Use;21
5.5.2;1.5.2 Process Description and Enactment;22
5.5.3;1.5.3 Autonomic Behaviour;22
5.5.4;1.5.4 Security and Trust;23
5.5.5;1.5.5 Annotation;24
5.5.6;1.5.6 Information Integration;24
5.5.7;1.5.7 Communities;25
5.6;1.6 A Semantic Grid Approach to In Silico Bioinformatics;25
5.7;1.7 A Semantic Datagrid for Chemistry;27
5.8;1.8 Architectures for the Semantic Grid;31
5.8.1;1.8.1 S-OGSA -- A Reference Architecture for the Semantic Grid;31
5.8.2;1.8.2 The Service-Oriented Knowledge Utility;34
5.8.3;1.8.3 The Semantic Grid Community;35
5.9;1.9 Moving Forward;36
5.10;1.10 Summary;37
5.11;References;38
6;2 Semantic Disclosure in an e-Science Environment;41
6.1;2.1 Introduction;41
6.1.1;2.1.1 Semantic Disclosure;41
6.1.2;2.1.2 The Semantic Web;42
6.1.3;2.1.3 Making Sense of the Digital Deluge;43
6.1.4;2.1.4 Data Integration;44
6.1.5;2.1.5 W3C Semantic Web for Health Care and Life Sciences Interest Group;44
6.1.6;2.1.6 Semantic Architecture;45
6.1.7;2.1.7 The Virtual Laboratory for e-Science Project;46
6.2;2.2 The AIDA Toolkit;47
6.2.1;2.2.1 Storage -- The Metadata Storage Module;48
6.2.2;2.2.2 Learning -- The Machine Learning Module;50
6.2.3;2.2.3 Search -- The Information Retrieval Module;51
6.3;2.3 Applications of Adaptive Information Disclosure;52
6.3.1;2.3.1 Food Informatics -- Adaptive Information Disclosure Collaboration;52
6.3.2;2.3.2 A Metadata Management Approach to fMRI Data;54
6.3.3;2.3.3 Semantic Disclosure in Support of Biological Experimentation;57
6.3.3.1;2.3.3.1 Application Case 1: Semantic Disclosure of Human Genome Data;57
6.3.3.2;2.3.3.2 Application Case 2: Semantic Disclosure of Biological Knowledge Trapped in Literature;61
6.3.3.3;2.3.3.3 An e-Science Approach for Extracting Knowledge from Text;62
6.3.3.4;2.3.3.4 Conclusion;71
6.4;2.4 Discussion;72
6.5;References;76
7;3 A Smart e-Science Cyberinfrastructure for Cross-Disciplinary Scientific Collaborations;78
7.1;3.1 Introduction;78
7.2;3.2 Background;81
7.2.1;3.2.1 Challenges for Smart e-Science;81
7.2.2;3.2.2 Enabling Technologies for Smart e-Science;82
7.2.2.1;3.2.2.1 Grid Computing;82
7.2.2.2;3.2.2.2 Service-Oriented Architecture and Web Services;82
7.2.2.3;3.2.2.3 Semantic Web and Ontology;83
7.2.3;3.2.3 Contemporary Efforts in e-Science Cyberinfrastructure;84
7.2.4;3.2.4 Case Study: Integration of Scientific Infrastructures;85
7.3;3.3 The Smart e-Science Framework;87
7.3.1;3.3.1 The e-Science Ontology;88
7.3.2;3.3.2 Grid-Based Service Orientation;93
7.3.3;3.3.3 Service Interface;93
7.3.4;3.3.4 Semantic Interface;94
7.3.5;3.3.5 The Proxy;94
7.3.6;3.3.6 Knowledgebase Design;95
7.3.7;3.3.7 Data Exchange;95
7.4;3.4 Implementation;97
7.4.1;3.4.1 Ontology Design;98
7.4.2;3.4.2 Server-Side Service Implementation;99
7.4.3;3.4.3 Client-Side Service Implementation;100
7.4.4;3.4.4 Fundamental and Composed Services;102
7.4.4.1;3.4.4.1 Users and Application Services;102
7.4.4.2;3.4.4.2 Sensor Resources Proxy Services;103
7.4.4.3;3.4.4.3 Compute/Data Resources Services;104
7.4.5;3.4.5 Conceptual and Semantic Schema;105
7.5;3.5 Conclusions;105
7.6;References;106
8;4 Developing Ontologies within Decentralised Settings;109
8.1;4.1 Introduction;109
8.1.1;4.1.1 Decentralised Communities;110
8.1.2;4.1.2 Community-Driven Ontology Engineering;111
8.1.3;4.1.3 Upper Level Ontologies;113
8.1.4;4.1.4 Dynamic Ontologies;113
8.1.5;4.1.5 The Melting Point: A Methodology for Distributed Community-Driven Ontology Engineering;114
8.2;4.2 Review of Current Methodologies;115
8.2.1;4.2.1 Criteria for Review;115
8.2.2;4.2.2 Finding the Melting Point;119
8.3;4.3 The Melting Point Methodology;123
8.3.1;4.3.1 Definition of Terminology;123
8.3.2;4.3.2 Management Processes;125
8.3.3;4.3.3 Documentation Processes;126
8.3.4;4.3.4 Development-Oriented Processes;127
8.3.5;4.3.5 Evaluation;131
8.4;4.4 Discussion;132
8.4.1;4.4.1 Melting Point Evaluated;132
8.4.2;4.4.2 IEEE Standards Compliance;133
8.4.3;4.4.3 Quality Assurance;133
8.4.4;4.4.4 Activities Become Interrelated;134
8.4.5;4.4.5 Recommended Life Cycle: Incremental Evolutionary Spiral;135
8.5;4.5 Conclusions;135
8.6;A. Appendix: Review of Methodologies;137
8.6.1;A.1 The Enterprise Methodology;137
8.6.2;A.2 The TOVE Methodology;138
8.6.3;A.3 The Bernaras Methodology;139
8.6.4;A.4 The METHONTOLOGY Methodology;140
8.6.5;A.5 The SENSUS Methodology;141
8.6.6;A.6 DILIGENT;142
8.6.7;A.7 The GM Methodology;143
8.6.8;A.8 The iCapturer Methodology;143
8.6.9;A.9 NeOn Methodology;145
8.7;References;145
9;5 Semantic Technologies for Searching in e-Science Grids;150
9.1;5.1 Introduction;150
9.1.1;5.1.1 e-Science or Scientific Cyber-Infrastructure;150
9.1.2;5.1.2 Scientific Cyber-Infrastructure Planning and Designing Challenges: Scope of Discussion;151
9.1.3;5.1.3 Role of Search and Semantic Technologies;153
9.1.4;5.1.4 Chapter Overview;154
9.2;5.2 Scientific Cyber-Infrastructure: Functional Requirements;154
9.2.1;5.2.1 Business Processes in Research;155
9.2.2;5.2.2 Cyber-Infrastructure Functional Blocks and Enabling Technologies;156
9.3;5.3 Search Technology for Cyber-Infrastructures;160
9.3.1;5.3.1 Search Basics;160
9.3.2;5.3.2 Search and Retrieval Performance Metrics;162
9.3.3;5.3.3 Existing Meaning-Based Search Techniques;163
9.3.4;5.3.4 Intention-Based Web Searching;163
9.4;5.4 Semantic Technologies: Requirements and Literature Survey;164
9.4.1;5.4.1 Crucial Semantic Technologies;164
9.4.2;5.4.2 Requirements for Semantic Technologies;164
9.4.3;5.4.3 A Study of Meaning in Human Cognition and Language;165
9.4.4;5.4.4 Meaning Representation in Computers: Existing Works;169
9.5;5.5 Proposed Semantic Technologies for Cyber-Infrastructure;175
9.5.1;5.5.1 Overview;176
9.5.2;5.5.2 Concept Tree Representation;177
9.5.3;5.5.3 Required Algebra;179
9.5.4;5.5.4 Tensor Representation of Concept Tree;182
9.5.5;5.5.5 Bloom Filter Basics;185
9.5.6;5.5.6 Generation of Bloom Filter-Based Descriptor Data Structure;186
9.5.7;5.5.7 Descriptor Comparison Algorithm;187
9.5.8;5.5.8 Extensions for Incorporating Synonym and Hypernyms;188
9.6;5.6 Discussions;191
9.7;5.7 Conclusion;192
9.8;References;193
10;6 BSIS: An Experiment in Automating Bioinformatics Tasks Through Intelligent Workflow Construction;197
10.1;6.1 Introduction;197
10.2;6.2 Related Work;198
10.2.1;6.2.1 Custom Scripts;198
10.2.2;6.2.2 Domain-Specific Programming Environments;199
10.2.3;6.2.3 Integrated Analysis Environments;199
10.2.4;6.2.4 Workflow Systems;200
10.3;6.3 An Overview of the BioService Integration System;201
10.3.1;6.3.1 The Web Service Infrastructure;201
10.3.2;6.3.2 The Workflow Language;202
10.3.3;6.3.3 The Planner;202
10.3.4;6.3.4 The Executor;202
10.4;6.4 The BSIS Web Service Infrastructure;202
10.4.1;6.4.1 Domain Ontologies;203
10.4.1.1;6.4.1.1 Service Ontology;203
10.4.1.2;6.4.1.2 Data Ontology;204
10.4.2;6.4.2 Services Description;205
10.4.3;6.4.3 Services Registry;208
10.5;6.5 Workflow Language;209
10.5.1;6.5.1 Entity Nodes;209
10.5.1.1;6.5.1.1 Service nodes;209
10.5.1.2;6.5.1.2 Data Nodes;211
10.5.1.3;6.5.1.3 Control Nodes;211
10.5.1.4;6.5.1.4 Operator Nodes;213
10.5.2;6.5.2 Connectors;213
10.5.3;6.5.3 Development of Sample Workflows;214
10.5.4;6.5.4 Workflow Language Formalization;216
10.5.5;6.5.5 A Prototype Implementation of the Workflow Language;217
10.6;6.6 The Planner;218
10.6.1;6.6.1 Objectives of the Planner;218
10.6.2;6.6.2 Service Mapping;219
10.6.3;6.6.3 Quality of Service;222
10.6.4;6.6.4 Data Binding;223
10.6.5;6.6.5 Data Conversion;225
10.6.5.1;6.6.5.1 Guided Search;226
10.6.5.2;6.6.5.2 Blind Search;226
10.6.6;6.6.6 Planning the Workflow;228
10.6.7;6.6.7 Planning with an External Planner;229
10.6.7.1;6.6.7.1 Situation Calculus;229
10.6.7.2;6.6.7.2 C-Golog;230
10.6.7.3;6.6.7.3 Workflows as Golog Programs;231
10.7;6.7 Executor;234
10.7.1;6.7.1 Execution Framework;234
10.7.2;6.7.2 Extension Mechanism;236
10.7.2.1;6.7.2.1 Web Service Providers;236
10.7.2.2;6.7.2.2 Custom Operator;237
10.7.3;6.7.3 BioPerl Modules;238
10.8;6.8 Case Studies and Optimizations;238
10.8.1;6.8.1 Some Case Studies;238
10.8.2;6.8.2 Optimization;240
10.9;6.9 Conclusion and Future Work;242
10.10;References;244
11;7 Near-Miss Detection in Nursing: Rules and Semantics;247
11.1;7.1 Introduction;247
11.2;7.2 Nursing Domain and Near Miss;249
11.2.1;7.2.1 Nursing;250
11.2.2;7.2.2 Bone Marrow Transplantation;251
11.2.3;7.2.3 Bone Marrow Transplantation Nursing;252
11.2.4;7.2.4 Human Factor and Near Miss;253
11.2.5;7.2.5 Service, SLA, Plans, Rules, Patterns;254
11.2.6;7.2.6 Real Life Event Ordering;255
11.2.7;7.2.7 Behaviour Formalisation;258
11.3;7.3 Technologies for Near Miss in Nursing;259
11.3.1;7.3.1 Identification;259
11.3.2;7.3.2 Ubiquity;262
11.3.3;7.3.3 Adaptivity;263
11.3.4;7.3.4 Sensing and Multi-sensor Fusion;264
11.3.5;7.3.5 Presence and Context;266
11.3.6;7.3.6 Uncertainty and Rule-Based Systems;267
11.3.7;7.3.7 Ontology Engineering in Bone Marrow Transplantation;269
11.4;7.4 Knowledge and Semantics;271
11.4.1;7.4.1 Nursing Process Modelling;273
11.4.2;7.4.2 Application;277
11.5;7.5 Implementation;280
11.5.1;7.5.1 Architecture;280
11.5.2;7.5.2 Selection of Components;282
11.5.3;7.5.3 Intelligent Layer;285
11.5.4;7.5.4 Feedback;286
11.5.5;7.5.5 Correlations;287
11.6;7.6 Conclusions;291
11.7;References;292
12;8 Toward Autonomous Mining of the Sensor Web;296
12.1;8.1 Introduction;296
12.2;8.2 Sensor Web with Semantics;297
12.3;8.3 Semantically Enabled Earth Science Model Service;299
12.4;8.4 Semantic Web Service-Based Approach;301
12.5;8.5 Process of Sensor Web Mining;303
12.6;8.6 Ontology-Based Knowledge Base;304
12.7;8.7 OGC Catalogue Service for Web (CS/W) with Semantic Augmentations;307
12.8;8.8 Geospatial Web Services;308
12.8.1;8.8.1 Data Services;308
12.8.2;8.8.2 Data Fusion Services;309
12.8.3;8.8.3 Earth Science Model Services;309
12.9;8.9 Mining Planner;310
12.10;8.10 BPEL Engine;311
12.11;8.11 Conclusions;312
12.12;References;313
13;9 Towards Knowledge-Based Life Science PublicationRepositories;315
13.1;9.1 Introduction;315
13.1.1;9.1.1 Motivation;316
13.1.2;9.1.2 State-of-the-Art Overview;317
13.1.3;9.1.3 Main Contributions and Structure of the Chapter;318
13.2;9.2 Overview of Our Approach;319
13.3;9.3 Emergent Knowledge Processing Framework;320
13.3.1;9.3.1 Empirical Knowledge Representation;320
13.3.2;9.3.2 Inference Services;326
13.3.3;9.3.3 Notes on the Theoretical Principles' Implementation;331
13.4;9.4 Processing the Publication Data;332
13.4.1;9.4.1 Data;332
13.4.2;9.4.2 Method;334
13.5;9.5 Using CORAAL;338
13.6;9.6 Preliminary Tests with Domain Experts;342
13.7;9.7 Related Work;345
13.7.1;9.7.1 Similar Approaches to Emergent Knowledge Processing;345
13.7.2;9.7.2 Conformance to the Semantic Web Standards;346
13.8;9.8 Conclusion and Future Work;347
13.9;References;349