现代计算技术与中医药信息处理 英文版【2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载】

现代计算技术与中医药信息处理 英文版PDF格式电子书版下载

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1 Overview of Knowledge Discovery in Traditional Chinese Medicine1

1.1 Introduction1

1.2 The State of the Art of TCM Data Resources3

1.2.1 Traditional Chinese Medical Literature Analysis and Retrieval System4

1.2.2 Figures and Photographs of Traditional Chinese Drug Database4

1.2.3 Database of Chinese Medical Formulae5

1.2.4 Database of Chemical Composition from Chinese Herbal Medicine5

1.2.5 Clinical Medicine Database5

1.2.6 TCM Electronic Medical Record Database6

1.3 Review of KDTCM Research6

1.3.1 Knowledge Discovery for CMF Research6

1.3.2 Knowledge Discovery for CHM Research11

1.3.3 Knowledge Discovery for Research of TCM Syndrome14

1.3.4 Knowledge Discovery for TCM Clinical Diagnosis16

1.4 Discussions and Future Directions19

1.5 Conclusions22

2 Integrative Mining of Traditional Chinese Medicine Literature and MEDLINE for Functional Gene Networks27

2.1 Introduction27

2.2 Connecting TCM Syndrome to Modern Biomedicine by Integrative Literature Mining29

2.3 Related Work on Biomedical Literature Mining30

2.4 Name Entity and Relation Extraction Methods33

2.4.1 Bubble-Bootstrapping Method33

2.4.2 Relation Weight Computing35

2.5 MeDisco/3S System36

2.6 Results38

2.6.1 Functional Gene Networks43

2.6.2 Functional Analysis of Genes from Syndrome Perspective45

2.7 Conclusions47

3 MapReduce-Based Network Motif Detection for Traditional Chinese Medicine53

3.1 Introduction53

3.2 Related Work54

3.3 MapReduce-Based Pattern Finding55

3.3.1 MRPF Framework55

3.3.2 Neighbor Vertices Finding and Pattern Initialization57

3.3.3 Pattern Extension58

3.3.4 Frequency Computing59

3.4 Application to Prescription Compatibility Structure Detection61

3.4.1 Motifs Detection Results61

3.4.2 Performance Analysis62

3.5 Conclusions64

4 Data Quality for Knowledge Discovery in Traditional Chinese Medicine67

4.1 Introduction67

4.2 Key Data Quality Dimensions in TCM69

4.2.1 Representation Granularity69

4.2.2 Representation Consistency69

4.2.3 Completeness70

4.3 Methods to Handle Data Quality Problems70

4.3.1 Handling Representation Granularity70

4.3.2 Handling Representation Consistency71

4.3.3 Handling Completeness72

4.4 Conclusions73

5 Service-Oriented Data Mining in Traditional Chinese Medicine75

5.1 Introduction75

5.2 Related Work76

5.2.1 Traditional Data Mining Software76

5.2.2 Data Mining Systems for Specific Field77

5.2.3 Distributed Data Mining Platform77

5.2.4 The Spora Demo78

5.3 System Architecture and Data Mining Service78

5.3.1 Hierarchical Structure78

5.3.2 Service Operator Organization80

5.3.3 User Interaction and Visualization81

5.4 Case Studies82

5.4.1 Case 1： Domain-Driven KDD Support for TCM82

5.4.2 Case 2： Data Mining Based on Distributed Resources84

5.4.3 Case 3： Data Mining Process as a Service84

5.5 Conclusions85

6 Semantic E-Science for Traditional Chinese Medicine87

6.1 Introduction87

6.2 Results89

6.2.1 System Architecture89

6.2.2 TCM Domain Ontology91

6.2.3 DartMapping93

6.2.4 DartSearch94

6.2.5 DartQuery95

6.2.6 TCM Service Coordination98

6.2.7 Knowledge Discovery Service98

6.2.8 DartFlow99

6.2.9 TCM Collaborative Research Scenario100

6.2.10 Task-Driven Information Allocation100

6.2.11 Collaborative Information Sharing101

6.2.12 Scientific Service Coordination102

6.3 Discussion102

6.4 Conclusions103

6.5 Methods103

6.5.1 TCM Ontology Engineering103

6.5.2 View-Based Semantic Mapping104

6.5.3 Semantic-Based Service Matchmaking105

7 Ontology Development for Unified Traditional Chinese Medical Language System109

7.1Introduction109

7.2The Principle and Knowledge System of TCM110

7.3What Is an Ontology？111

7.4Protege 2000： The Tool We Use111

7.5Ontology Design and Development for UTCMLS112

7.5.1 Methodology of Ontology Development113

7.5.2 Knowledge Acquisition115

7.5.3 Integrating and Merging of TCM Ontology117

7.6 Results117

7.6.1 The Core Top-Level Categories120

7.6.2 Subontologies and the Hierarchical Structure120

7.6.3 Concept Structure120

7.6.4 Semantic Structure121

7.6.5 Semantic Types and Semantic Relationships121

7.7 Conclusions124

8 Causal Knowledge Modeling for Traditional Chinese Medicine Using OWL 2129

8.1 Introduction129

8.2 Causal TCM Knowledge Modeling130

8.3 Causal Reasoning130

8.4 Evaluation131

8.5 Conclusions132

9 Dynamic Subontology Evolution for Traditional Chinese Medicine Web Ontology135

9.1 Introduction135

9.2 TCM Domain Ontology136

9.2.1 Ontology Framework136

9.2.2 User Interface139

9.3 Subontology Model140

9.3.1 Preliminaries142

9.3.2 Subontology Definition143

9.3.3 Subontology Operators144

9.4 Ontology Cache for Knowledge Reuse146

9.4.1 Reusing Subontologies as Ontology Cache146

9.4.2 Knowledge Search with Ontology Cache147

9.4.3 On SubO Structural Optimality151

9.5 Dynamic Subontology Evolution152

9.5.1 Chromosome Representation152

9.5.2 Fitness Evaluation154

9.5.3 Genetic Operators154

9.5.4 Evolution Procedure157

9.5.5 Consistency158

9.6 Experiment and Evaluation158

9.6.1 Experiment Design158

9.6.2 Compare Cache Performance160

9.6.3 Knowledge Structure163

9.6.4 Traversal Depth for SubO Extraction164

9.7 Related Work165

9.8 Conclusions166

10 Semantic Association Mining for Traditional Chinese Medicine171

10.1 Introduction171

10.1.1 The Semantic Web for Collaborative Knowledge Discove171

10.1.2 The Motivating Story172

10.1.3 HerbNet： The Knowledge Network for Herbal Medicine173

10.1.4 Paper Organization174

10.2 Related Work174

10.2.1 Domain-Driven Relationship Mining for Biomedicine174

10.2.2 Linked Data on the Semantic Web175

10.2.3 Semantic Association Mining176

10.3 Methods177

10.3.1 Semantic Graph Model177

10.3.2 Hypothesis and Hypothetical Graph178

10.3.3 Evidence and Evidentiary Graph179

10.3.4 Semantic Schema181

10.3.5 Semantic Association Mining182

10.3.6 Semantic Association Ranking184

10.3.7 Summary185

10.4 Evaluation185

10.4.1 Synthetic Graph Generation186

10.4.2 Engine Implementation186

10.4.3 Miner Implementation187

10.4.4 Collaborative Discovery Process189

10.4.5 Result Analysis190

10.5 Use Cases191

10.5.1 The HerbNet192

10.5.2 Formula System Interpretation193

10.5.3 Herb—Drug Interaction Network Analysis194

10.6 Conclusions195

11 Semantic-Based Database Integration for Traditional Chinese Medicine199

11.1 Introduction199

11.2 System Architecture and Technical Features201

11.2.1 System Architecture201

11.2.2 Technical Features201

11.3 Semantic Mediation202

11.3.1 Semantic View and View-Based Mapping202

11.3.2 Visualized Semantic Mapping Tool204

11.4 TCM Semantic Portals205

11.4.1 Dynamic Semantic Query Interface205

11.4.2 Intuitive Search Interface with Concepts Ranking and Semantic Navigation206

11.5 User Evaluation and Lesson Learned208

11.5.1 Feedback from CATCM208

11.5.2 A Survey on the Usage of RDF/OWL Predicates209

11.6 Related Work209

11.6.1 Semantic Web Context209

11.6.2 Conventional Data Integration Context211

11.7 Conclusions211

12 Probabilistic Semantic Relationship Discovery from Traditional Chinese Medical Literature213

12.1 Background213

12.2 Related Work214

12.3 Methods215

12.3.1 Instance Extraction215

12.3.2 Instance Pair Discovery215

12.3.3 Semantic Relationship Evaluation217

12.3.4 Probability-Based Semantic Relationship Extraction218

12.4 Results and Discussions220

12.5 Conclusions221

13 Deriving Similarity Graphs from Traditional Chinese Medicine Linked Data on the Semantic Web223

13.1 Introduction223

13.2 Related Work224

13.2.1 Taxonomy-Based Approach224

13.2.2 Relationship-Based Approach224

13.3 SST Approach225

13.3.1 Similarity Transition225

13.3.2 Similarity between Sets of Objects226

13.4 Experiments and Results227

13.4.1 Dataset Preparation228

13.4.2 Results Analysis229

13.4.3 Result Visualization231

13.5 Conclusions232