| Preface |
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xi | |
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3 | (10) |
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What Is Semantic Data Modeling? |
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4 | (3) |
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Why Develop and Use a Semantic Data Model? |
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7 | (1) |
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8 | (2) |
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10 | (1) |
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11 | (2) |
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2 Semantic Modeling Elements |
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13 | (22) |
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14 | (9) |
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14 | (2) |
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16 | (1) |
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17 | (2) |
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19 | (2) |
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Complex Axioms, Constraints, and Rules |
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21 | (1) |
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22 | (1) |
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Common and Standardized Elements |
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23 | (9) |
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Lexicalization and Synonymy |
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24 | (1) |
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25 | (1) |
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Meaning Inclusion and Class/Relation Subsumption |
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26 | (1) |
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26 | (2) |
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28 | (1) |
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Mapping and Interlinking Relations |
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28 | (1) |
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29 | (3) |
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32 | (3) |
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3 Semantic And Linguistic Phenomena |
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35 | (14) |
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35 | (2) |
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37 | (1) |
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38 | (3) |
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Rigidity, Identity, Unity, and Dependence |
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41 | (1) |
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Symmetry, Inversion, and Transitivity |
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42 | (1) |
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Closed- and Open-World Assumptions |
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43 | (1) |
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43 | (3) |
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46 | (3) |
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49 | (14) |
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50 | (2) |
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52 | (2) |
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54 | (1) |
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55 | (2) |
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57 | (1) |
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57 | (1) |
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58 | (1) |
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59 | (1) |
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Availability, Versatility, and Performance |
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60 | (1) |
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61 | (2) |
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5 Semantic Model Development |
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63 | (24) |
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63 | (8) |
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64 | (3) |
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67 | (1) |
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68 | (1) |
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69 | (1) |
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69 | (1) |
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70 | (1) |
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Vocabularies, Patterns, and Exemplary Models |
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71 | (5) |
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71 | (1) |
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72 | (2) |
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Standard and Reference Models |
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74 | (1) |
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Public Models and Datasets |
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74 | (2) |
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76 | (8) |
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76 | (3) |
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Mining Methods and Techniques |
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79 | (5) |
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84 | (3) |
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87 | (22) |
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87 | (5) |
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89 | (1) |
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90 | (1) |
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91 | (1) |
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Omitting Definitions or Giving Bad Ones |
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92 | (5) |
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When You Need Definitions |
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93 | (1) |
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94 | (1) |
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95 | (1) |
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96 | (1) |
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97 | (10) |
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Vagueness Is a Feature, Not a Bug |
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99 | (1) |
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Detecting and Describing Vagueness |
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100 | (7) |
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Not Documenting Biases and Assumptions |
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107 | (1) |
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Keeping Your Enemies Close |
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107 | (1) |
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108 | (1) |
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109 | (16) |
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109 | (6) |
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110 | (3) |
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Bad Mapping and Interlinking |
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113 | (2) |
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115 | (4) |
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Instantiation as Subclassing |
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115 | (2) |
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117 | (1) |
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Rigid Classes as Subclasses of Nonrigid Classes |
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118 | (1) |
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Common Superclasses with Incompatible Identity Criteria |
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119 | (1) |
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119 | (4) |
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Defining Hierarchical Relations as Transitive |
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119 | (2) |
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Defining Vague Relations as Transitive |
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121 | (1) |
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Complementary Vague Classes |
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121 | (1) |
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Mistaking Inference Rules for Constraints |
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122 | (1) |
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123 | (2) |
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8 Bad Model Specification And Knowledge Acquisition |
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125 | (30) |
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125 | (8) |
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Why We Get Bad Specifications |
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126 | (2) |
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How to Get the Right Specifications |
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128 | (5) |
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Bad Knowledge Acquisition |
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133 | (13) |
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134 | (6) |
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Wrong Acquisition Methods and Tools |
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140 | (6) |
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A Specification and Knowledge Acquisition Story |
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146 | (7) |
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Model Specification and Design |
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146 | (3) |
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149 | (4) |
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153 | (2) |
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155 | (14) |
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Not Treating Quality as a Set of Trade-Offs |
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155 | (3) |
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Semantic Accuracy Versus Completeness |
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156 | (1) |
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Conciseness Versus Completeness |
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156 | (1) |
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Conciseness Versus Understandability |
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157 | (1) |
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Relevancy to Context A Versus Relevancy to Context B |
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157 | (1) |
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Not Linking Quality to Risks and Benefits |
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158 | (2) |
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Not Using the Right Metrics |
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160 | (7) |
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Using Metrics with Misleading Interpretations |
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160 | (2) |
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Using Metrics with Little Comparative Value |
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162 | (1) |
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Using Metrics with Arbitrary Value Thresholds |
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162 | (2) |
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Using Metrics That Are Actually Quality Signals |
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164 | (1) |
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Measuring Accuracy of Vague Assertions in a Crisp Way |
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165 | (1) |
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Equating Model Quality with Information Extraction Quality |
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166 | (1) |
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167 | (2) |
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169 | (20) |
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169 | (12) |
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How Entity Resolution Systems Use Semantic Models |
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170 | (1) |
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When Knowledge Can Hurt You |
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171 | (1) |
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How to Select Disambiguation-Useful Knowledge |
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172 | (6) |
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Two Entity Resolution Stories |
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178 | (3) |
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181 | (6) |
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Why Semantic Relatedness Is Tricky |
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182 | (1) |
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How to Get the Semantic Relatedness You Really Need |
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183 | (1) |
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A Semantic Relatedness Story |
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184 | (3) |
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187 | (2) |
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11 Bad Strategy And Organization |
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189 | (16) |
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189 | (7) |
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What Is a Semantic Model Strategy About? |
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190 | (2) |
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Buying into Myths and Half-Truths |
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192 | (1) |
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Underestimating Complexity and Cost |
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193 | (2) |
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Not Knowing or Applying Your Context |
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195 | (1) |
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196 | (6) |
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Not Building the Right Team |
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196 | (4) |
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Underestimating the Need for Governance |
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200 | (2) |
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202 | (3) |
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12 Representation Dilemmas |
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205 | (22) |
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205 | (3) |
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To Subclass or Not to Subclass? |
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208 | (3) |
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211 | (1) |
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To Fuzzify or Not to Fuzzify? |
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212 | (13) |
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What Fuzzification Involves |
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212 | (8) |
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220 | (2) |
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Two Fuzzification Stories |
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222 | (3) |
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225 | (2) |
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13 Expressiveness And Content Dilemmas |
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227 | (16) |
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What Lexicalizations to Have? |
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227 | (4) |
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231 | (2) |
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233 | (2) |
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235 | (2) |
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How Many Truths to Handle? |
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237 | (1) |
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238 | (3) |
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241 | (2) |
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14 Evolution And Governance Dilemmas |
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243 | (18) |
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243 | (12) |
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244 | (1) |
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244 | (2) |
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246 | (3) |
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Knowing and Acting on Your Semantic Drift |
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249 | (6) |
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255 | (3) |
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Democracy, Oligarchy, or Dictatorship? |
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255 | (2) |
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257 | (1) |
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258 | (3) |
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261 | (8) |
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The Map Is Not the Territory |
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261 | (1) |
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Being an Optimist, but Not Naive |
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262 | (1) |
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263 | (1) |
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Avoiding Distracting Debates |
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263 | (3) |
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Semantic Versus Nonsemantic Frameworks |
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263 | (2) |
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Symbolic Knowledge Representation Versus Machine Learning |
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265 | (1) |
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266 | (1) |
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Bridging the Semantic Gap |
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267 | (2) |
| Bibliography |
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269 | (22) |
| Glossary |
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291 | (4) |
| Index |
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295 | |
