Knowledge graphs: fundamentals, techniques, and applications
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
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Cambridge, Massachusetts ; London, England
The MIT Press
[2021]
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxvii, 530 Seiten Illustrationen |
| ISBN: | 9780262045094 |
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Datensatz im Suchindex
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| adam_text | Contents List of Figures List of Tables Preface I KNOWLEDGE GRAPH FUNDAMENTALS 1 Introduction to Knowledge Graphs 1.1 1.2 1.3 2 xv xxiii xxv 3 Graphs Representing Knowledge as Graphs Examples of Knowledge Graphs 1.3.1 Example 1 : Scientific Publications and Academics 3 5 10 10 1.4 1.5 1.6 1.3.2 Example 2: E-Commerce, Products, and Companies 1.3.3 Example 3: Social Networks 1.3.4 Example 4: Geopolitical Events How to Read This Text Concluding Notes Software and Resources 11 12 12 14 15 15 1.7 1.8 Bibliographic Notes Exercises 16 18 Modeling and Representing Knowledge Graphs 2.1 Introduction 2.1.1 Resource Description Framework 2.1.2 RDF Serializations 2.2 RDF Schema 2.2.1 2.2.2 RDFS Classes RDFS Properties 21 21 23 26 28 28 30
vi Contents 2.3 2.4 Property-Centric Models Wikidata Model 2.4.1 Wikidata Items 2.4.2 Wikidata Properties 31 34 37 38 2.5 2.6 2.7 2.8 2.9 2.10 The Semantic Web Layer Cake Schema Heterogeneity and Semantic Labeling Concluding Notes Software and Resources Bibliographic Notes Exercises 40 42 44 44 46 47 II KNOWLEDGE GRAPH CONSTRUCTION 3 Domain Discovery 53 3.1 Introduction 3.2 Focused Crawling 3.2.1 Main Design Elementsof a Focused Crawler 3.2.2 Best-First Crawlers 3.2.3 Semantic Crawlers 3.2.4 Learning Crawlers 3.2.5 Evaluation of Focused Crawling 3.3 Influential Systems and Methodologies 3.3.1 Context-Focused Crawler 3.3.2 Domain Discovery Tool 3.4 Concluding Notes 3.5 Software and Resources 3.6 Bibliographic Notes 3.7 Exercises 53 56 57 59 60 62 63 64 64 67 71 71 73 75 Named Entity Recognition 77 4.1 Introduction 4.2 Why Is Information Extraction Hard? 4.3 Approaches for Named Entity Recognition 4.3.1 Supervised Approaches 4.3.2 Semisupervised and Unsupervised Approaches 4.4 Deep Learning for Named Entity Recognition 4.5 Domain-Specific Named Entity Recognition 4.6 Evaluating Information Extraction Quality 77 80 82 84 87 89 91 92 4
Contents 4.7 4.8 4.9 4.10 5 Concluding Notes Software and Resources Bibliographic Notes Exercises 93 93 94 96 Web Information Extraction 97 5.1 Introduction 5.2 Wrapper Generation 5.2.1 Manually Constructed and SupervisedWrappers 5.2.2 Semisupervised Approaches 5.2.3 Unsupervised Approaches 5.2.4 Empirical Comparative Analyses 5.3 Beyond Wrappers: Information Extraction over Structured Data 97 102 102 106 107 111 113 5.4 5.5 5.6 5.7 6 vii Concluding Notes Software and Resources Bibliographic Notes Exercises 120 121 122 123 Relation Extraction 125 Introduction Ontologies and Programs 6.2.1 Automatic Content Extraction 6.2.2 Other Ontologies: A Brief Primer 6.3 Techniques for Relation Extraction 6.3.1 Supervised Relation Extraction 6.3.2 Evaluating Supervised Relation Extraction 6.3.3 Semisupervised Relation Extraction 6.3.4 Unsupervised Relation Extraction 6.4 Recent Research: Deep Learning for Relation Extraction 6.5 Beyond Relation Extraction: Event Extraction and Joint Information Extraction 125 127 128 130 131 131 135 135 138 139 6.1 6.2 6.6 6.7 6.8 6.9 Concluding Notes Software and Resources Bibliographic Notes Exercises 143 144 144 146 147
Contents viii 7 Nontraditional Information Extraction 149 7.1 7.2 149 151 Introduction Open Information Extraction 7.2.1 7.2.2 7.2.3 7.3 7.4 KnowItAll TextRunner Evaluating and Comparing Open Information Extraction Systems 159 Social Media Information Extraction 7.3.1 TWICAL 161 163 7.3.2 TwitlE Other Kinds of Nontraditional Information Extraction 164 166 7.5 Concluding Notes 7.6 Software and Resources 7.7 157 158 Bibliographie Notes 7.8 Exercises III KNOWLEDGE GRAPH COMPLETION 8 Instance Matching 167 169 170 171 175 8.1 Introduction 175 8.2 8.3 8.4 Formalism Why Is Instance Matching Challenging? Two-Step Pipeline 8.4.1 Blocking 8.4.2 Similarity 178 179 180 180 190 8.5 Evaluating the Two-Step Pipeline 194 8.5.1 8.5.2 195 197 Evaluating Blocking Evaluating Similarity Postsimilarity Steps 8.6.1 Clustering and Transitive Closure 8.6.2 Entity Name System Formalizing Instance Matching: Swoosh A Note on Research Frontiers 198 198 201 203 205 8.9 Data Cleaning beyond Instance Matching 8.10 Concluding Notes 208 212 8.11 Software and Resources 8.12 Bibliographic Notes 213 215 8.6 8.7 8.8
Contents 9 8.13 Exercises 217 Statistical Relational Learning 221 9.1 Introduction 9.2 Modeling Dependencies 9.3 Statistical Relational Learning Frameworks 9.3.1 Markov Logic Networks 9.3.2 Probabilistic Soft Logic 9.4 Knowledge Graph Identification 9.4.1 Representing Uncertain Extractions 9.4.2 Representing Instance Matching Outputs 9.4.3 Enforcement of Ontological Constraints 9.4.4 Putting It Together: Probabilistic Distributions over Uncertain Knowledge Graphs 234 9.4.5 A Note on Experimental Performance 9.5 Other Applications 9.5.1 Collective Classification 9.5.2 Link Prediction 9.5.3 Social Network Modeling 9.6 Advanced Research: Data Programming 221 223 224 225 231 232 233 233 234 9.7 9.8 9.9 9.10 10 ix Concluding Notes Software and Resources Bibliographic Notes Exercises 235 235 235 236 236 236 237 238 238 239 Representation Learning for Knowledge Graphs 241 10.1 Introduction 10.2 Embedding Architectures: A Primer 10.2.1 Continuous Bag of Words Model 10.2.2 Skip-Gram Model 10.3 Embeddings beyond Words 10.4 Knowledge Graph Embeddings 10.4.1 Energy Functions 241 243 245 246 246 248 250 10.5 Influential KGE Systems 10.5.1 Structured Embeddings 10.5.2 Neural Tensor Networks 251 252 254 10.5.3 Translational Embedding Models 10.5.4 TransE 256 256
x Contents 10.5.5 Other Trans* Algorithms 259 10.6 Extrafactual Contexts 10.6.1 Entity Types 10.6.2 Textual Data 10.6.3 Beyond Text and Concepts: Other Information Sets 262 263 264 265 10.7 Applications 10.7.1 Link Prediction 267 267 10.7.2 10.7.3 10.7.4 10.7.5 268 268 269 270 Triple Classification Entity Classification Revisiting Instance Matching Other Applications 10.8 Concluding Notes 10.9 Software and Resources 10.10 Bibliographic Notes 10.11 Exercises 270 271 272 273 IV ACCESSING KNOWLEDGE GRAPHS 11 Reasoning and Retrieval 279 11.1 Introduction 11.2 Reasoning 11.2.1 Description Logics: A Brief Primer 11.2.2 Web Ontology Language 11.2.3 Sample Reasoning Framework: Protege 11.3 Retrieval 11.3.1 Term Frequency and Weighting 11.4 Retrieval versus Reasoning 11.4.1 Evaluation 11.4.2 Sample Information Retrieval Framework: Lucene 11.5 Concluding Notes 11.6 Software and Resources 11.7 Bibliographic Notes 11.8 Exercises 279 281 283 284 289 291 292 293 295 300 302 303 303 305 Structured Querying 307 12 12.1 Introduction 12.2 SPARQL 12.2.1 Subqueries 307 308 310
Contents xi 12.3 Relational Processing of Queries over Knowledge Graphs 12.3.1 Triple (Vertical) Stores 312 12.3.2 Property Table Stores 313 12.3.3 Horizontal Stores 314 12.4 NoSQL 12.4.1 Key-Value Stores 13 311 316 316 12.4.2 Graph Databases 321 12.4.3 NoSQL Databases with Extreme Scalability 328 12.5 Concluding Notes 330 12.6 Software and Resources 330 12.7 Bibliographic Notes 332 12.8 Exercises 333 Question Answering 337 13.1 Introduction 337 13.2 Question Answering as a Stand-Alone Application 339 13.2.1 Learning from Conversational Dialogue: KnowBot 339 13.2.2 Bidirectional Encoder Representations fromTransformers 341 13.2.3 Necessity of Knowledge Graphs 345 13.3 Question Answering as Knowledge Graph Querying 346 13.3.1 Challenges and Solutions 347 13.3.2 Template-Based Solutions 355 13.3.3 Evaluation of SQA 356 13.4 Concluding Notes 358 13.5 Software and Resources 358 13.5.1 BERT and Language Model-Based QuestionAnswering 359 13.5.2 HOBBIT 359 13.6 Bibliographic Notes 360 13.7 Exercises 362 V KNOWLEDGE GRAPHECOSYSTEMS 14 Linked Data 14.1 Introduction 367 367 14.1.1 Principle 1 : Use Uniform Resource Identifiers for Naming Things 372 14.1.2 Principle 2:UseHTTP Uniform Resource Identifiers 373
Contents xii 14.1.3 Principle 3: Provide Useful Information on Lookup Using Standards 14.1.4 Principle 4: Link NewData to Existing Data 14.2 Impact and Adoption of Linked Data Principles 377 14.2.1 Overall Impact 14.3 Important Knowledge Graphs in Linked Open Data 379 379 14.3.1 14.3.2 14.3.3 14.3.4 14.3.5 380 380 381 382 383 384 385 Concluding Notes Software and Resources Bibliographic Notes Exercises 386 386 387 388 Enterprise and Government 391 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 16 DBpedia GeoNames YAGO Wikidata Upper Mapping and Binding Exchange Layer 14.3.6 Friend of a Friend 14.3.7 Other Examples 14.4 14.5 14.6 14.7 15 375 376 Introduction Enterprise 15.2.1 Knowledge Vault 15.2.2 Social Media and Open Graph Protocol 15.2.3 Schema.org Governments and Nonprofits 15.3.1 Open Government Data 15.3.2 BBC 15.3.3 OpenStreetMap Where Is the Future Headed? Concluding Notes Software and Resources Bibliographic Notes Exercises 391 392 393 399 399 402 402 405 405 407 408 409 410 412 Knowledge Graphs and Ontologies in Science 415 16.1 Introduction 16.2 Biology 16.2.1 Gene Ontology 415 417 417
Contents 16.3 Chemistry 16.3.1 Chemical Entities of Biological Interest 16.3.2 PubChem 16.4 Earth, Environment, and Geosciences 16.4.1 Semantic Web for Earth and Environmental Terminology 16.4.2 The GEON Portal and OpenTopography 16.4.3 Environment Ontology 16.5 Concluding Notes 16.6 Software and Resources 16.7 Bibliographic Notes 16.8 Exercises 17 Knowledge Graphs for Domain-Specific Social Impact xiii 423 423 426 427 427 427 431 433 434 435 436 439 17.1 Introduction 17.2 Domain-Specific Insight Graphs 17.2.1 Domain Setup 17.2.2 Domain Exploration 17.3 Alternative System: DeepDive 17.4 Applications and Use-Cases 17.4.1 Investigative Domains 17.4.2 Crisis Informatics 17.4.3 COVID-19 and Medical Informatics 17.5 Concluding Notes 17.6 Software and Resources 17.7 Bibliographic Notes 17.8 Exercises 439 441 442 446 450 452 452 456 458 460 461 463 465 Bibliography Index 467 511
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| adam_txt |
Contents List of Figures List of Tables Preface I KNOWLEDGE GRAPH FUNDAMENTALS 1 Introduction to Knowledge Graphs 1.1 1.2 1.3 2 xv xxiii xxv 3 Graphs Representing Knowledge as Graphs Examples of Knowledge Graphs 1.3.1 Example 1 : Scientific Publications and Academics 3 5 10 10 1.4 1.5 1.6 1.3.2 Example 2: E-Commerce, Products, and Companies 1.3.3 Example 3: Social Networks 1.3.4 Example 4: Geopolitical Events How to Read This Text Concluding Notes Software and Resources 11 12 12 14 15 15 1.7 1.8 Bibliographic Notes Exercises 16 18 Modeling and Representing Knowledge Graphs 2.1 Introduction 2.1.1 Resource Description Framework 2.1.2 RDF Serializations 2.2 RDF Schema 2.2.1 2.2.2 RDFS Classes RDFS Properties 21 21 23 26 28 28 30
vi Contents 2.3 2.4 Property-Centric Models Wikidata Model 2.4.1 Wikidata Items 2.4.2 Wikidata Properties 31 34 37 38 2.5 2.6 2.7 2.8 2.9 2.10 The Semantic Web Layer Cake Schema Heterogeneity and Semantic Labeling Concluding Notes Software and Resources Bibliographic Notes Exercises 40 42 44 44 46 47 II KNOWLEDGE GRAPH CONSTRUCTION 3 Domain Discovery 53 3.1 Introduction 3.2 Focused Crawling 3.2.1 Main Design Elementsof a Focused Crawler 3.2.2 Best-First Crawlers 3.2.3 Semantic Crawlers 3.2.4 Learning Crawlers 3.2.5 Evaluation of Focused Crawling 3.3 Influential Systems and Methodologies 3.3.1 Context-Focused Crawler 3.3.2 Domain Discovery Tool 3.4 Concluding Notes 3.5 Software and Resources 3.6 Bibliographic Notes 3.7 Exercises 53 56 57 59 60 62 63 64 64 67 71 71 73 75 Named Entity Recognition 77 4.1 Introduction 4.2 Why Is Information Extraction Hard? 4.3 Approaches for Named Entity Recognition 4.3.1 Supervised Approaches 4.3.2 Semisupervised and Unsupervised Approaches 4.4 Deep Learning for Named Entity Recognition 4.5 Domain-Specific Named Entity Recognition 4.6 Evaluating Information Extraction Quality 77 80 82 84 87 89 91 92 4
Contents 4.7 4.8 4.9 4.10 5 Concluding Notes Software and Resources Bibliographic Notes Exercises 93 93 94 96 Web Information Extraction 97 5.1 Introduction 5.2 Wrapper Generation 5.2.1 Manually Constructed and SupervisedWrappers 5.2.2 Semisupervised Approaches 5.2.3 Unsupervised Approaches 5.2.4 Empirical Comparative Analyses 5.3 Beyond Wrappers: Information Extraction over Structured Data 97 102 102 106 107 111 113 5.4 5.5 5.6 5.7 6 vii Concluding Notes Software and Resources Bibliographic Notes Exercises 120 121 122 123 Relation Extraction 125 Introduction Ontologies and Programs 6.2.1 Automatic Content Extraction 6.2.2 Other Ontologies: A Brief Primer 6.3 Techniques for Relation Extraction 6.3.1 Supervised Relation Extraction 6.3.2 Evaluating Supervised Relation Extraction 6.3.3 Semisupervised Relation Extraction 6.3.4 Unsupervised Relation Extraction 6.4 Recent Research: Deep Learning for Relation Extraction 6.5 Beyond Relation Extraction: Event Extraction and Joint Information Extraction 125 127 128 130 131 131 135 135 138 139 6.1 6.2 6.6 6.7 6.8 6.9 Concluding Notes Software and Resources Bibliographic Notes Exercises 143 144 144 146 147
Contents viii 7 Nontraditional Information Extraction 149 7.1 7.2 149 151 Introduction Open Information Extraction 7.2.1 7.2.2 7.2.3 7.3 7.4 KnowItAll TextRunner Evaluating and Comparing Open Information Extraction Systems 159 Social Media Information Extraction 7.3.1 TWICAL 161 163 7.3.2 TwitlE Other Kinds of Nontraditional Information Extraction 164 166 7.5 Concluding Notes 7.6 Software and Resources 7.7 157 158 Bibliographie Notes 7.8 Exercises III KNOWLEDGE GRAPH COMPLETION 8 Instance Matching 167 169 170 171 175 8.1 Introduction 175 8.2 8.3 8.4 Formalism Why Is Instance Matching Challenging? Two-Step Pipeline 8.4.1 Blocking 8.4.2 Similarity 178 179 180 180 190 8.5 Evaluating the Two-Step Pipeline 194 8.5.1 8.5.2 195 197 Evaluating Blocking Evaluating Similarity Postsimilarity Steps 8.6.1 Clustering and Transitive Closure 8.6.2 Entity Name System Formalizing Instance Matching: Swoosh A Note on Research Frontiers 198 198 201 203 205 8.9 Data Cleaning beyond Instance Matching 8.10 Concluding Notes 208 212 8.11 Software and Resources 8.12 Bibliographic Notes 213 215 8.6 8.7 8.8
Contents 9 8.13 Exercises 217 Statistical Relational Learning 221 9.1 Introduction 9.2 Modeling Dependencies 9.3 Statistical Relational Learning Frameworks 9.3.1 Markov Logic Networks 9.3.2 Probabilistic Soft Logic 9.4 Knowledge Graph Identification 9.4.1 Representing Uncertain Extractions 9.4.2 Representing Instance Matching Outputs 9.4.3 Enforcement of Ontological Constraints 9.4.4 Putting It Together: Probabilistic Distributions over Uncertain Knowledge Graphs 234 9.4.5 A Note on Experimental Performance 9.5 Other Applications 9.5.1 Collective Classification 9.5.2 Link Prediction 9.5.3 Social Network Modeling 9.6 Advanced Research: Data Programming 221 223 224 225 231 232 233 233 234 9.7 9.8 9.9 9.10 10 ix Concluding Notes Software and Resources Bibliographic Notes Exercises 235 235 235 236 236 236 237 238 238 239 Representation Learning for Knowledge Graphs 241 10.1 Introduction 10.2 Embedding Architectures: A Primer 10.2.1 Continuous Bag of Words Model 10.2.2 Skip-Gram Model 10.3 Embeddings beyond Words 10.4 Knowledge Graph Embeddings 10.4.1 Energy Functions 241 243 245 246 246 248 250 10.5 Influential KGE Systems 10.5.1 Structured Embeddings 10.5.2 Neural Tensor Networks 251 252 254 10.5.3 Translational Embedding Models 10.5.4 TransE 256 256
x Contents 10.5.5 Other Trans* Algorithms 259 10.6 Extrafactual Contexts 10.6.1 Entity Types 10.6.2 Textual Data 10.6.3 Beyond Text and Concepts: Other Information Sets 262 263 264 265 10.7 Applications 10.7.1 Link Prediction 267 267 10.7.2 10.7.3 10.7.4 10.7.5 268 268 269 270 Triple Classification Entity Classification Revisiting Instance Matching Other Applications 10.8 Concluding Notes 10.9 Software and Resources 10.10 Bibliographic Notes 10.11 Exercises 270 271 272 273 IV ACCESSING KNOWLEDGE GRAPHS 11 Reasoning and Retrieval 279 11.1 Introduction 11.2 Reasoning 11.2.1 Description Logics: A Brief Primer 11.2.2 Web Ontology Language 11.2.3 Sample Reasoning Framework: Protege 11.3 Retrieval 11.3.1 Term Frequency and Weighting 11.4 Retrieval versus Reasoning 11.4.1 Evaluation 11.4.2 Sample Information Retrieval Framework: Lucene 11.5 Concluding Notes 11.6 Software and Resources 11.7 Bibliographic Notes 11.8 Exercises 279 281 283 284 289 291 292 293 295 300 302 303 303 305 Structured Querying 307 12 12.1 Introduction 12.2 SPARQL 12.2.1 Subqueries 307 308 310
Contents xi 12.3 Relational Processing of Queries over Knowledge Graphs 12.3.1 Triple (Vertical) Stores 312 12.3.2 Property Table Stores 313 12.3.3 Horizontal Stores 314 12.4 NoSQL 12.4.1 Key-Value Stores 13 311 316 316 12.4.2 Graph Databases 321 12.4.3 NoSQL Databases with Extreme Scalability 328 12.5 Concluding Notes 330 12.6 Software and Resources 330 12.7 Bibliographic Notes 332 12.8 Exercises 333 Question Answering 337 13.1 Introduction 337 13.2 Question Answering as a Stand-Alone Application 339 13.2.1 Learning from Conversational Dialogue: KnowBot 339 13.2.2 Bidirectional Encoder Representations fromTransformers 341 13.2.3 Necessity of Knowledge Graphs 345 13.3 Question Answering as Knowledge Graph Querying 346 13.3.1 Challenges and Solutions 347 13.3.2 Template-Based Solutions 355 13.3.3 Evaluation of SQA 356 13.4 Concluding Notes 358 13.5 Software and Resources 358 13.5.1 BERT and Language Model-Based QuestionAnswering 359 13.5.2 HOBBIT 359 13.6 Bibliographic Notes 360 13.7 Exercises 362 V KNOWLEDGE GRAPHECOSYSTEMS 14 Linked Data 14.1 Introduction 367 367 14.1.1 Principle 1 : Use Uniform Resource Identifiers for Naming Things 372 14.1.2 Principle 2:UseHTTP Uniform Resource Identifiers 373
Contents xii 14.1.3 Principle 3: Provide Useful Information on Lookup Using Standards 14.1.4 Principle 4: Link NewData to Existing Data 14.2 Impact and Adoption of Linked Data Principles 377 14.2.1 Overall Impact 14.3 Important Knowledge Graphs in Linked Open Data 379 379 14.3.1 14.3.2 14.3.3 14.3.4 14.3.5 380 380 381 382 383 384 385 Concluding Notes Software and Resources Bibliographic Notes Exercises 386 386 387 388 Enterprise and Government 391 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 16 DBpedia GeoNames YAGO Wikidata Upper Mapping and Binding Exchange Layer 14.3.6 Friend of a Friend 14.3.7 Other Examples 14.4 14.5 14.6 14.7 15 375 376 Introduction Enterprise 15.2.1 Knowledge Vault 15.2.2 Social Media and Open Graph Protocol 15.2.3 Schema.org Governments and Nonprofits 15.3.1 Open Government Data 15.3.2 BBC 15.3.3 OpenStreetMap Where Is the Future Headed? Concluding Notes Software and Resources Bibliographic Notes Exercises 391 392 393 399 399 402 402 405 405 407 408 409 410 412 Knowledge Graphs and Ontologies in Science 415 16.1 Introduction 16.2 Biology 16.2.1 Gene Ontology 415 417 417
Contents 16.3 Chemistry 16.3.1 Chemical Entities of Biological Interest 16.3.2 PubChem 16.4 Earth, Environment, and Geosciences 16.4.1 Semantic Web for Earth and Environmental Terminology 16.4.2 The GEON Portal and OpenTopography 16.4.3 Environment Ontology 16.5 Concluding Notes 16.6 Software and Resources 16.7 Bibliographic Notes 16.8 Exercises 17 Knowledge Graphs for Domain-Specific Social Impact xiii 423 423 426 427 427 427 431 433 434 435 436 439 17.1 Introduction 17.2 Domain-Specific Insight Graphs 17.2.1 Domain Setup 17.2.2 Domain Exploration 17.3 Alternative System: DeepDive 17.4 Applications and Use-Cases 17.4.1 Investigative Domains 17.4.2 Crisis Informatics 17.4.3 COVID-19 and Medical Informatics 17.5 Concluding Notes 17.6 Software and Resources 17.7 Bibliographic Notes 17.8 Exercises 439 441 442 446 450 452 452 456 458 460 461 463 465 Bibliography Index 467 511 |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV047291738 |
| illustrated | Illustrated |
| index_date | 2024-07-03T17:20:22Z |
| indexdate | 2024-07-10T09:07:59Z |
| institution | BVB |
| isbn | 9780262045094 |
| language | English |
| lccn | 2020028169 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032695084 |
| oclc_num | 1257815310 |
| open_access_boolean | |
| owner | DE-29T DE-N2 DE-355 DE-BY-UBR DE-739 |
| owner_facet | DE-29T DE-N2 DE-355 DE-BY-UBR DE-739 |
| physical | xxvii, 530 Seiten Illustrationen |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | The MIT Press |
| record_format | marc |
| spelling | Kejriwal, Mayank Verfasser (DE-588)1232859362 aut Knowledge graphs fundamentals, techniques, and applications Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekely Cambridge, Massachusetts ; London, England The MIT Press [2021] xxvii, 530 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Information visualization Wissensrepräsentation (DE-588)4049534-6 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Graph (DE-588)4021842-9 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Künstliche Intelligenz (DE-588)4033447-8 s Wissensrepräsentation (DE-588)4049534-6 s Graph (DE-588)4021842-9 s DE-604 Knoblock, Craig A. 1962- Verfasser (DE-588)172191041 aut Szekely, Pedro Verfasser (DE-588)1232860964 aut Erscheint auch als Online-Ausgabe 978-0-262-36188-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032695084&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kejriwal, Mayank Knoblock, Craig A. 1962- Szekely, Pedro Knowledge graphs fundamentals, techniques, and applications Information visualization Wissensrepräsentation (DE-588)4049534-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Graph (DE-588)4021842-9 gnd |
| subject_GND | (DE-588)4049534-6 (DE-588)4033447-8 (DE-588)4021842-9 (DE-588)4123623-3 |
| title | Knowledge graphs fundamentals, techniques, and applications |
| title_auth | Knowledge graphs fundamentals, techniques, and applications |
| title_exact_search | Knowledge graphs fundamentals, techniques, and applications |
| title_exact_search_txtP | Knowledge graphs fundamentals, techniques, and applications |
| title_full | Knowledge graphs fundamentals, techniques, and applications Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekely |
| title_fullStr | Knowledge graphs fundamentals, techniques, and applications Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekely |
| title_full_unstemmed | Knowledge graphs fundamentals, techniques, and applications Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekely |
| title_short | Knowledge graphs |
| title_sort | knowledge graphs fundamentals techniques and applications |
| title_sub | fundamentals, techniques, and applications |
| topic | Information visualization Wissensrepräsentation (DE-588)4049534-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Graph (DE-588)4021842-9 gnd |
| topic_facet | Information visualization Wissensrepräsentation Künstliche Intelligenz Graph Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032695084&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kejriwalmayank knowledgegraphsfundamentalstechniquesandapplications AT knoblockcraiga knowledgegraphsfundamentalstechniquesandapplications AT szekelypedro knowledgegraphsfundamentalstechniquesandapplications |