Knowledge graphs:
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
[San Rafael, California]
Morgan & Claypool Publishers
[2022]
|
| Schriftenreihe: | Synthesis lectures on data, semantics, and knowledge
# 22 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xix, 237 Seiten Illustrationen |
| ISBN: | 9781636392356 9781636392370 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1805069089554563072 |
|---|---|
| adam_text |
ix Contents Preface . xv Acknowledgments . xix ļ Introduction. 1 2 Data Graphs. 5 2.1 Models. 5 2.1.1 Directed Edge-LabeledGraphs. 6 2.1.2 Heterogeneous Graphs. 8 2.1.3 2.1.4 2.1.5 2.1.6 2.2 3 Property Graphs. 9 Graph Dataset.11 Other Graph Data Models. 12 Graph Stores. 13 Querying. 13 2.2.1 Basic Graph Patterns .13 2.2.2 Complex
Grajih Patterns. 16 2.2.3 Navigational Graph Patterns . 19 2.2.4 Other Features . 22 2.2.5 Query Interfaces. 22 Schema, Identity,and Context. 25 3.1 Schema. 25 3.1.1 Semantic Schema.25 3.1.2 Validating Schema . 27 3.1.3 Emergent Schema. 32 3.2 identity. 35 3.2.1 Persistent Identifiers. 35 3.2.2 External Identity 1 .inks. 37 3.2.3 Datatypes. 38 3.2.4
Lexicalization. 38 3.2.5 Existential Nodes. 39
3.3 4 Context.40 3.3.1 Direct Representation. 40 3.3.2 Reification. 41 3.3.3 Higher-Arity Representation. 41 3.3.4 Annotations. 42 3.3.5 Other Contextual Frameworks. 44 Deductive Knowledge . 47 4.1 4.2 Ontologies . 48 4.1.1 Interpretations and Models .49 4.1.2 Ontology Features. 51 4.1.3 Entailment . 56 4.1.4 If-Then vs. If-and-Only-If Semantics. 56 Reasoning. 57 4.2.1
Rules.57 4.2.2 Description Logics. 60 5 Inductive Knowledge. 67 5.1 5.2 5.3 Graph Analytics. 67 5.1.1 Techniques .69 5.1.2 Frameworks. 70 5.1.3 5.1.4 Analytics on Data Graphs . 74 Analytics with Queries. 75 5.1.5 Analytics with Entailment.76 Knowledge Graph Embeddings. 76 5.2.1 Tensor-Based Models. 78 5.2.2 Language Models. 88 5.2.3 Entailment-Aware Models. 90 Graph Neural Networks. 91
5.3.1 Recursive Graph Neural Networks. 91 5.3.2 Non-Recursive Graph Neural Networks. 95 5.4 Symbolic Learning. 96 5.4.1 Rule Mining. 97 5.4.2 Axiom Mining .100 5.4.3 1 lypothesis Mining. 102
xi 6 7 Creation and Enrichment.105 6.1 1 luman Collaboration. 105 6.2 Text Sources. 106 6.2.1 Pre-Processing. 106 6.2.2 Named Entity Recognition (NER) . 107 6.2.3 Entity Linking (EE). 107 6.2.4 Relation Extraction (RE). 103 6.2.5 Joint Tasks. 109 6.3 Markup Sources. 109 6.3.1 Wrapper-Based Extraction. 110 6.3.2 Web Table Extraction. 111 6.3.3 Deep Web Crawling. 111 6.4 Structured Sources. 111 6.4.1 Mapping from
Tables. 112 6.4.2 Mapping from Trees.114 6.4.3 Mapping from Other Knowledge Graphs. 114 6.5 Schema/Ontology Creation. ID 6.5.1 Ontology Engineering. 115 6.5.2 Ontology Learning. 116 Quality Assessment. 119 7.1 Accuracy. 119 7.1.1 Syntactic Accuracy. 119 7.1.2 Semantic Accuracy. 120 7.1.3 Timeliness.120 7.2 Coverage. 121 7.2.1 Completeness . 121 7.2.2 Representativeness . 121 7.3
Coherency. 122 7.3.1 Consistency. 122 7.3.2 Validity . 123 7.4 Succinctness. 125 7.4.1 Conciseness. 122 7.4.2 Representational Conciseness. 124 7.4.3 Understandability. 12՜! 7.5 Other Quality Dimensions . 125
8 9 10 Refinement. 127 8.1 Completion. 127 8.1.1 Generiti Link Prediction. 127 8.1.2 Type-Link Prediction. 128 8.1.3 Identity-Link Prediction.128 8.2 Correction. 129 8.2.1 Fact Validation. 129 8.2.2 Inconsistency Repairs. 131 8.3 Other Refinement Tasks . 131 Publication. 133 9.1 Best Practices. 133 9.1.1 FAIR Principles . 133 9.1.2 Linked Data Principles.135 9.2 Access 9.2.1 9.2.2 9.2.3 9.2.4 9.2.5
Protocols. 137 Dumps. 137 Node Lookups. 138 Edge Patterns. 138 (Complex) Graph Patterns. 139 Other Protocols. 140 9.3 Usage 9.3.1 9.3.2 9.3.3 9.3.4 Control. 140 Incensing. . 140 Usage Policies. 141 Encryption . 142 Anonymization. 143 Knowledge Graphs in Practice . 145 10.1 Open KnowledgeGraphs . 145 10.1.1 DBpedia .
. 145 10.1.2 Yet Another Great Ontology. 146 10.1.3 Freebase. 147 10.1.4 Wikidata. 147 10.1.5 Other Open Cross-Domain Knowledge Graphs.148 10.1.6 Domain-Specific Open Knowledge Graphs . 148 10.2 Enterprise Knowledge Graphs. 149 10.2.1 Web Search. 149 10.2.2 Commerce. 150
xiii 10.2.3 Social Networks . 150 10.2.4 Innance . 151 10.2.5 Other Industries. 151 11 Conclusions . 153 Background. 157 Л.1 Historical Perspective. 157 Л.2 “Knowledge Graphs:” Pre֊2012. 15S Л.З “Knowledge Graphs:” 2012 Onward. 161 Bibliography. 165 Authors’ Biographies. 229 |
| adam_txt |
ix Contents Preface . xv Acknowledgments . xix ļ Introduction. 1 2 Data Graphs. 5 2.1 Models. 5 2.1.1 Directed Edge-LabeledGraphs. 6 2.1.2 Heterogeneous Graphs. 8 2.1.3 2.1.4 2.1.5 2.1.6 2.2 3 Property Graphs. 9 Graph Dataset.11 Other Graph Data Models. 12 Graph Stores. 13 Querying. 13 2.2.1 Basic Graph Patterns .13 2.2.2 Complex
Grajih Patterns. 16 2.2.3 Navigational Graph Patterns . 19 2.2.4 Other Features . 22 2.2.5 Query Interfaces. 22 Schema, Identity,and Context. 25 3.1 Schema. 25 3.1.1 Semantic Schema.25 3.1.2 Validating Schema . 27 3.1.3 Emergent Schema. 32 3.2 identity. 35 3.2.1 Persistent Identifiers. 35 3.2.2 External Identity 1 .inks. 37 3.2.3 Datatypes. 38 3.2.4
Lexicalization. 38 3.2.5 Existential Nodes. 39
3.3 4 Context.40 3.3.1 Direct Representation. 40 3.3.2 Reification. 41 3.3.3 Higher-Arity Representation. 41 3.3.4 Annotations. 42 3.3.5 Other Contextual Frameworks. 44 Deductive Knowledge . 47 4.1 4.2 Ontologies . 48 4.1.1 Interpretations and Models .49 4.1.2 Ontology Features. 51 4.1.3 Entailment . 56 4.1.4 If-Then vs. If-and-Only-If Semantics. 56 Reasoning. 57 4.2.1
Rules.57 4.2.2 Description Logics. 60 5 Inductive Knowledge. 67 5.1 5.2 5.3 Graph Analytics. 67 5.1.1 Techniques .69 5.1.2 Frameworks. 70 5.1.3 5.1.4 Analytics on Data Graphs . 74 Analytics with Queries. 75 5.1.5 Analytics with Entailment.76 Knowledge Graph Embeddings. 76 5.2.1 Tensor-Based Models. 78 5.2.2 Language Models. 88 5.2.3 Entailment-Aware Models. 90 Graph Neural Networks. 91
5.3.1 Recursive Graph Neural Networks. 91 5.3.2 Non-Recursive Graph Neural Networks. 95 5.4 Symbolic Learning. 96 5.4.1 Rule Mining. 97 5.4.2 Axiom Mining .100 5.4.3 1 lypothesis Mining. 102
xi 6 7 Creation and Enrichment.105 6.1 1 luman Collaboration. 105 6.2 Text Sources. 106 6.2.1 Pre-Processing. 106 6.2.2 Named Entity Recognition (NER) . 107 6.2.3 Entity Linking (EE). 107 6.2.4 Relation Extraction (RE). 103 6.2.5 Joint Tasks. 109 6.3 Markup Sources. 109 6.3.1 Wrapper-Based Extraction. 110 6.3.2 Web Table Extraction. 111 6.3.3 Deep Web Crawling. 111 6.4 Structured Sources. 111 6.4.1 Mapping from
Tables. 112 6.4.2 Mapping from Trees.114 6.4.3 Mapping from Other Knowledge Graphs. 114 6.5 Schema/Ontology Creation. ID 6.5.1 Ontology Engineering. 115 6.5.2 Ontology Learning. 116 Quality Assessment. 119 7.1 Accuracy. 119 7.1.1 Syntactic Accuracy. 119 7.1.2 Semantic Accuracy. 120 7.1.3 Timeliness.120 7.2 Coverage. 121 7.2.1 Completeness . 121 7.2.2 Representativeness . 121 7.3
Coherency. 122 7.3.1 Consistency. 122 7.3.2 Validity . 123 7.4 Succinctness. 125 7.4.1 Conciseness. 122 7.4.2 Representational Conciseness. 124 7.4.3 Understandability. 12՜! 7.5 Other Quality Dimensions . 125
8 9 10 Refinement. 127 8.1 Completion. 127 8.1.1 Generiti Link Prediction. 127 8.1.2 Type-Link Prediction. 128 8.1.3 Identity-Link Prediction.128 8.2 Correction. 129 8.2.1 Fact Validation. 129 8.2.2 Inconsistency Repairs. 131 8.3 Other Refinement Tasks . 131 Publication. 133 9.1 Best Practices. 133 9.1.1 FAIR Principles . 133 9.1.2 Linked Data Principles.135 9.2 Access 9.2.1 9.2.2 9.2.3 9.2.4 9.2.5
Protocols. 137 Dumps. 137 Node Lookups. 138 Edge Patterns. 138 (Complex) Graph Patterns. 139 Other Protocols. 140 9.3 Usage 9.3.1 9.3.2 9.3.3 9.3.4 Control. 140 Incensing. . 140 Usage Policies. 141 Encryption . 142 Anonymization. 143 Knowledge Graphs in Practice . 145 10.1 Open KnowledgeGraphs . 145 10.1.1 DBpedia .
. 145 10.1.2 Yet Another Great Ontology. 146 10.1.3 Freebase. 147 10.1.4 Wikidata. 147 10.1.5 Other Open Cross-Domain Knowledge Graphs.148 10.1.6 Domain-Specific Open Knowledge Graphs . 148 10.2 Enterprise Knowledge Graphs. 149 10.2.1 Web Search. 149 10.2.2 Commerce. 150
xiii 10.2.3 Social Networks . 150 10.2.4 Innance . 151 10.2.5 Other Industries. 151 11 Conclusions . 153 Background. 157 Л.1 Historical Perspective. 157 Л.2 “Knowledge Graphs:” Pre֊2012. 15S Л.З “Knowledge Graphs:” 2012 Onward. 161 Bibliography. 165 Authors’ Biographies. 229 |
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| title | Knowledge graphs |
| title_auth | Knowledge graphs |
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| title_full | Knowledge graphs Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Malo [und 13 weitere] |
| title_fullStr | Knowledge graphs Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Malo [und 13 weitere] |
| title_full_unstemmed | Knowledge graphs Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Malo [und 13 weitere] |
| title_short | Knowledge graphs |
| title_sort | knowledge graphs |
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