Factorization models for multi-relational data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Göttingen
Cuvillier
2014
|
| Ausgabe: | 1. Aufl. |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XIII, 114 S. graph. Darst. 21 cm |
| ISBN: | 9783736947344 9783954047345 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text |
CONTENTS
1 INTRODUCTION 1
1.1 MULTI-RELATIONAL LEARNING 1
1.1.1 FACTORIZATION MODELS FOR MULTI-RELATIONAL DATA 4
1.1.2 APPLICATIONS OF MULTI RELATIONAL LEARNING 5
1.2 CONTRIBUTION 6
1.3 SUBMITTED AND PUBLISHED WORK 7
1.4 CHAPTER OVERVIEW 9
2 THE MULTI-RELATIONAL FACTORIZATION PROBLEM 11
2.1 PROBLEM FORMULATION 12
2.2 STATE OF THE ART 14
2.2.1 PARAMETRIZATION OF MULTI-RELATIONAL FACTORIZATION MODELS 15
2.2.2 OPTIMIZATION OBJECTIVES FOR MULTI-RELATIONAL LEARNING . 18
2.2.3 SUMMARY OF THE PRESENTED MODELS 21
2.3 EVALUATING MULTI-RELATIONAL LEARNING MODELS 21
2.4 OPEN PROBLEMS IN MULTI-RELATIONAL FACTORIZATION 23
3 LOSS FUNCTIONS FOR MULTI-RELATIONAL LEARNING TASKS 26
3.1 APPLICATION SCENARIO: MINING RDF KNOWLEDGE BASES 27
3.1.1 RDF INFERENCE AND RELATED WORK 28
3.2 PREDICTING RDF TRIPLES 30
3.3 PREDICTING RDF TRIPLES BY TENSOR FACTORIZATION 31
3.3.1 FACTORIZATION MODELS 31
3.3.1.1 THREE-WAY INTERACTION MODEL 31
3.3.1.2 PAIRWISE INTERACTION MODEL 32
3.3.2 THE OPEN WORLD ASSUMPTION AND THE LOSS FUNCTION 33
3.3.3 DEALING WITH THE OPEN WORLD ASSUMPTION THROUGH THE BPR
FRAMEWORK 35
3.3.4 FILTERING THE RESULTS OF TENSOR FACTORIZATION MODELS . 36
V
HTTP://D-NB.INFO/1052949436
CONTENTS
3.4 EVALUATION 39
3.4.1 DATASETS 39
3.4.2 METHODS 40
3.4.3 EVALUATION METHODOLOGY 41
3.4.4 RESULTS 42
3.4.5 COMPARING BPR AGAINST RMSE 44
3.4.6 EVALUATION OF TYPE INFORMATION THROUGH POST FILTER . 45
3.5 CONCLUSIONS 50
4 TARGET-SPECIFIC PARAMETRIZATION OF MULTI-RELATIONAL MODELS 51
4.1 LEARNING WITH MULTIPLE TARGET RELATIONS 52
4.2 MULTI-TARGET FACTORIZATION 53
4.2.1 OPTIMIZING MODELS FOR MULTIPLE TARGET RELATIONS 54
4.2.2 COUPLED AUXILIARY AND TARGET SPECIFIC FACTORIZATION . 57
4.2.3 SETTING UP CATSMF 60
4.3 EVALUATION 62
4.3.1 COMPARISON AGAINST THE STATE-OF-THE-ART 62
4.3.2 EVALUATION PROTOCOL AND METRICS 64
4.3.3 EXPERIMENT I: BENCHMARK DATASETS 66
4.3.4 EXPERIMENT II: WEB DATASETS 67
4.4 CONCLUSION 74
5 FACTORIZATION MODELS FOR SEMI-SUPERVISED CLASSIFICATION 76
5.1 SEMI-SUPERVISED CLASSIFICATION 77
5.2 RELATED WORK 80
5.2.1 SEMI-SUPERVISED LEARNING 80
5.2.2 MULTI-RELATIONAL FACTORIZATION FOR SEMI-SUPERVISED CLASSI
FICATION 82
5.3 SEMI-SUPERVISED CLASSIFICATION PROBLEM FORMULATION 83
5.4 FACTORIZATION MODELS FOR SEMI-SUPERVISED CLASSIFICATION 85
5.4.1 NEIGHBORHOOD BASED FEATURE EXTRACTION 86
5.4.2 SEMI-SUPERVISED LEARNING OF PNT-CMF 88
5.4.3 LEARNING INDUCTIVE FACTORIZATION MODELS FOR CLASSIFICATION 90
5.5 EVALUATION 91
5.5.1 DATASETS 92
5.5.2 SETUP 92
5.5.3 BASELINES 93
5.5.4 MODEL SELECTION 94
5.5.5 RESULTS AND DISCUSSION 94
VI
CONTENTS
5.6 CONCLUSION 98
6 CONCLUSION 100
6.1 SUMMARY 100
6.2 DISCUSSION 101
6.3 FUTURE DIRECTION 102
INDEX 106
REFERENCES 114
VII |
| any_adam_object | 1 |
| author | Rêgo Drumond, Lucas |
| author_GND | (DE-588)1058457128 |
| author_facet | Rêgo Drumond, Lucas |
| author_role | aut |
| author_sort | Rêgo Drumond, Lucas |
| author_variant | d l r dl dlr |
| building | Verbundindex |
| bvnumber | BV042453251 |
| classification_rvk | ST 270 ST 300 |
| ctrlnum | (OCoLC)883388706 (DE-599)DNB1052949436 |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| edition | 1. Aufl. |
| format | Thesis Book |
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| indexdate | 2024-09-10T01:40:03Z |
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| isbn | 9783736947344 9783954047345 |
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| physical | XIII, 114 S. graph. Darst. 21 cm |
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| spelling | Rêgo Drumond, Lucas Verfasser (DE-588)1058457128 aut Factorization models for multi-relational data Lucas Rêgo Drumond 1. Aufl. Göttingen Cuvillier 2014 XIII, 114 S. graph. Darst. 21 cm txt rdacontent n rdamedia nc rdacarrier Zugl.: Hildesheim, Univ., Diss., 2014 Faktorisierung (DE-588)4128927-4 gnd rswk-swf Teilüberwachtes Lernen (DE-588)4782452-9 gnd rswk-swf Automatische Klassifikation (DE-588)4120957-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Relationale Datenbank (DE-588)4049358-1 gnd rswk-swf Factorization Models Relational Learning (DE-588)4113937-9 Hochschulschrift gnd-content Relationale Datenbank (DE-588)4049358-1 s Faktorisierung (DE-588)4128927-4 s Maschinelles Lernen (DE-588)4193754-5 s Automatische Klassifikation (DE-588)4120957-6 s Teilüberwachtes Lernen (DE-588)4782452-9 s DE-604 Erscheint auch als Online-Ausgabe 978-3-7369-4734-4 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4699907&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027888496&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rêgo Drumond, Lucas Factorization models for multi-relational data Faktorisierung (DE-588)4128927-4 gnd Teilüberwachtes Lernen (DE-588)4782452-9 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Relationale Datenbank (DE-588)4049358-1 gnd |
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| title | Factorization models for multi-relational data |
| title_auth | Factorization models for multi-relational data |
| title_exact_search | Factorization models for multi-relational data |
| title_full | Factorization models for multi-relational data Lucas Rêgo Drumond |
| title_fullStr | Factorization models for multi-relational data Lucas Rêgo Drumond |
| title_full_unstemmed | Factorization models for multi-relational data Lucas Rêgo Drumond |
| title_short | Factorization models for multi-relational data |
| title_sort | factorization models for multi relational data |
| topic | Faktorisierung (DE-588)4128927-4 gnd Teilüberwachtes Lernen (DE-588)4782452-9 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Relationale Datenbank (DE-588)4049358-1 gnd |
| topic_facet | Faktorisierung Teilüberwachtes Lernen Automatische Klassifikation Maschinelles Lernen Relationale Datenbank Hochschulschrift |
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