Semantic snippets via query-biased ranking of linked data entities:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Passau
2017
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| Schlagworte: | |
| Online-Zugang: | Volltext Volltext Inhaltsverzeichnis |
| Beschreibung: | XIX, 148 Seiten Illustrationen, Diagramme |
Internformat
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| 245 | 1 | 0 | |a Semantic snippets via query-biased ranking of linked data entities |c Mazen Alsarem |
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Datensatz im Suchindex
| _version_ | 1804176992967327744 |
|---|---|
| adam_text | Contents
Acknowledgements v
Abstract ix
Résumé xi
Zusammenfassung xiii
List of Figures xix
List of Tables xxi
1 Introduction 1
1.1 Context.................................................................. 5
1.2 Background............................................................... 6
1.2.1 The Web .......................................................... 6
1.2.2 Web of Data........................................................ 6
1.2.3 Co-existence of the two web (web of documents and web of data) . 8
1.2.4 Web Information Retrieval .........................................10
1.2.5 Search User Interface..............................................12
1.3 Statement of the Problem.................................................14
1.4 Research Questions.......................................................15
1.5 Application Scenarios ...................................................16
1.5.1 Application Scenario 1: General Vs. Specific user information need 16
1.5.2 Application Scenario 2: Answering directly (in SERF) hard ques-
tions .............................................................19
1.6 Summary of Contributions.................................................24
1.6.1 Query-biased Ranking for Linked Data Entities......................24
1.6.2 “Semantic Snippets and the Enhanced Search Engine (ENsEN) . 25
1.7 Structure of the Thesis..................................................27
2 A Query-biased Ranking for LOD Entities 29
2.1 Introduction and Context.................................................30
2.2 Related Works............................................................31
2.2.1 Comparative study..................................................33
Xlll
2.2.2 Selected approaches from the literature........................38
2.2.3 Conclusion.....................................................49
2.3 Our Algorithm (LDRANK)................................................50
2.3.1 Context........................................................50
2.3.2 Prior Knowledge Derived from the Ranking Provided by the Web
Search Engine Result Page......................................51
2.3.3 Prior Knowledge Derived from an Iterative Latent Semantic Anal-
ysis of the Textual Data Describing the Entities......................51
2.3.4 Belief Aggregation Strategy....................................53
2.3.5 LDRANK.........................................................54
2.4 Evaluation............................................................54
2.4.1 Introduction ..................................................54
2.4.2 Build the evaluation dataset...................................55
2.4.3 Experiments ...................................................58
2.4.4 Results and discussion.........................................59
2.5 Conclusion ...........................................................59
3 The Semantic Snippets 61
3.1 Introduction..........................................................62
3.2 Related Work..........................................................63
3.2.1 Enhancing Snippets for the Semantic Web........................63
3.2.2 Enhancing Snippets for the Web of documents....................64
3.3 Elements composing a Semantic Snippet.................................66
3.4 Semantic Snippet as an Interactive Artifact...........................67
3.5 Semantic Snippets4 5 Generation.......................................69
3.6 Learning to rank documents excerpts..................................71
3.6.1 Background.....................................................71
3.6.2 Related Work...................................................76
3.6.3 Feature Engineering............................................78
3.6. 4 Training Dataset...............................................81
3.6.5 Re-Balancing the Training Dataset..............................82
3.6.6 First Results..................................................83
3.6.7 Results using other regrouping strategies......................83
3.6.8 Feature Selection..............................................84
3.6.9 Selected Features..............................................85
3.6.10 Conclusion.....................................................86
4 Enhanced Search Engine (ENsEN) 87
4.1 Introduction..........................................................88
4.2 Software Design and Architecture......................................88
4.2.1 External Services..............................................88
4.2.2 High-level Architecture .......................................90
4.2.3 Internal Components............................................90
4.2.4 Data Model.....................................................99
4.2.5 Workflow..................................................... 101
4.3 User Interface.......................................................103
4.3.1 Visual Design.................................................104
4.3.2 ENsEN design and Crowdsourcing...............................104
4.4 Implementation......................................................106
4.4.1 Technological and architectural choices......................106
4.4.2 Hardware Configurations .....................................108
4.5 Crowdsourcing Evaluation of ENsEN...................................108
4.5.1 Methodology..................................................108
4.5.2 Results Analysis.............................................Ill
4.6 Conclusion.........................................................116
5 Conclusion 117
5.1 Research summary....................................................118
5.2 Future Work and Perspectives........................................120
5.2.1 LDRÂNK: Exploring more prior knowledge sources...............120
5.2.2 Semantic Index...............................................122
5.2.3 SERP from documents to concepts..............................123
5.2.4 Personalization and Recommendation...........................124
5.2.5 ENsEN from prototype to product..............................125
A Crowdsourcing Microtask Example for the Evaluation of ENsEN s
search interface. 127
A.l Introduction........................................................128
A.2 Microtask...........................................................129
A.3 Used topics and questions...........................................134
B i bli ogr aphy 137
Publications List
147
|
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| author | Alsarem, Mazen |
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| discipline | Informatik |
| format | Thesis Book |
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| spelling | Alsarem, Mazen Verfasser aut Semantic snippets via query-biased ranking of linked data entities Mazen Alsarem Passau 2017 XIX, 148 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Dissertation Universität Passau 2016 Ranking (DE-588)4307945-3 gnd rswk-swf Suchmaschinenoptimierung (DE-588)1026698669 gnd rswk-swf Suchmaschine (DE-588)4423007-2 gnd rswk-swf World Wide Web 3.0 (DE-588)1031634185 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content World Wide Web 3.0 (DE-588)1031634185 s Suchmaschine (DE-588)4423007-2 s Ranking (DE-588)4307945-3 s Suchmaschinenoptimierung (DE-588)1026698669 s DE-604 Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:739-opus4-3959 https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/395 kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bvb:739-opus4-3959 Resolving-System Volltext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029414649&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Alsarem, Mazen Semantic snippets via query-biased ranking of linked data entities Ranking (DE-588)4307945-3 gnd Suchmaschinenoptimierung (DE-588)1026698669 gnd Suchmaschine (DE-588)4423007-2 gnd World Wide Web 3.0 (DE-588)1031634185 gnd |
| subject_GND | (DE-588)4307945-3 (DE-588)1026698669 (DE-588)4423007-2 (DE-588)1031634185 (DE-588)4113937-9 |
| title | Semantic snippets via query-biased ranking of linked data entities |
| title_auth | Semantic snippets via query-biased ranking of linked data entities |
| title_exact_search | Semantic snippets via query-biased ranking of linked data entities |
| title_full | Semantic snippets via query-biased ranking of linked data entities Mazen Alsarem |
| title_fullStr | Semantic snippets via query-biased ranking of linked data entities Mazen Alsarem |
| title_full_unstemmed | Semantic snippets via query-biased ranking of linked data entities Mazen Alsarem |
| title_short | Semantic snippets via query-biased ranking of linked data entities |
| title_sort | semantic snippets via query biased ranking of linked data entities |
| topic | Ranking (DE-588)4307945-3 gnd Suchmaschinenoptimierung (DE-588)1026698669 gnd Suchmaschine (DE-588)4423007-2 gnd World Wide Web 3.0 (DE-588)1031634185 gnd |
| topic_facet | Ranking Suchmaschinenoptimierung Suchmaschine World Wide Web 3.0 Hochschulschrift |
| url | https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/395 https://nbn-resolving.org/urn:nbn:de:bvb:739-opus4-3959 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029414649&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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