Clustering and ranking for web information retrieval: methodologies for searching the web
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Saarbrücken
VDM Verl., Müller
2008
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Teilw. zugl.: Pisa, Univ., Diss., 2006 |
| Physical Description: | 137 S. graph. Darst. |
| ISBN: | 9783836456579 |
Staff View
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Record in the Search Index
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| adam_text | CONTENTS INTRODUCTION 9 AN OVERVIEW OF WEB INFORMATION RETRIEVAL 15 1.1
AN OVERVIEW OF TRADITIONAL IR 15 1.2 MODERN WEBLR IT 1.3 SIZE OF THE WEB
1!) 1.3.1 ESTIMATING SIZES AND OVERLAPS OF SEARCH ENGINES 1!) 1.3.2
ESTIMATING THE INDEXABLE WEB 21 AN OVERVIEW OF WEB SNIPPET CLUSTERING 23
2.1 BASIC DEFINITION AND NOTATION 24 2.1.1 SIMILARITY AND DISSIMILARITY
24 2.1.2 PARTITION APPROACHES 24 2.1.3 GEOMETRIC APPROACHES 26 2.1.4
SYNTACTICAL CLUSTERING 27 2.1.5 PROBABILISTIC APPROACHES AND OTHER
METHODS 28 2.1.6 CLUSTERING REQUIREMENTS AND LIMITATIONS 28 2.2 WEB
SNIPPET CLUSTERING 29 2.2.1 THE COMMERCIAL SCENARIO 2!) 2.2.2 THE
SCIENTIFIC SCENARIO 30 2.2.3 A DEEP OVERVIEW OF THE MOST IMPORTANT
CLUSTERING ALGORITHMS 32 AN OVERVIEW OF WEB RANKING 41 3.1 BASIC
DEFINITION AND NOTATION 41 3.2 THE MATHEMATIC BEHIND TRADITIONAL WEB
RANKING 42 3.2.1 PAGERANK 44 3.2.2 HITS 45 3.2.3 SALSA 47 3.3
PERSONALIZED WEB RANKING 48 3.4 EXTENSIONS TO THE TRADITIONAL WEB
METRICS 50 3.5 ACCELERATING THE COMPUTATION OF PAGERANK 50 3.6 RANKING
ALGORITHMS BASED ON QUERY LEARNING 55 SNAKET: SNIPPET AGGREGATION FOR
KNOWLEDGE EXTRACTION 57 4.1 OUR CONTRIBUTION 58 4.2 THE ANATOMY OF
SNAKET. THE CLUSTERING ENGINE 60 4.2.1 TWO KNOWLEDGE BASES 61 4.2.2 THE
FIRST MODULE: THE SNIPPET ANALYZER 64 4.2. BIBLIOGRAFISCHE INFORMATIONEN
HTTP://D-NB.INFO/987753223 DIGITALISIERT DURCH BIBLIOGRAPHY 125 4.4
EXPERIMENTAL RESULTS 76 4.4.1 USERS SURVEYS 77 4.4.2 SNAKET S DATASET
AND ANECDOTAL EVIDENCE 77 4.4.3 EVALUATION OF SNAKET 78 4.5 CONCLUSIONS
81 5 RANKING A STREAM OF NEWS 85 5.1 OUR CONTRIBUTION 87 5.2 STIME
DESIDERATA 88 5.3 A MODEL FOR NEWS ARTICLES 89 5.4 ALGORITHMS FOR NEWS
ARTICLES AND NEWS SOURCES 90 5.4.1 TIME-UNAWARE RANKING ALGORITHMS 91
5.4.2 TIME-AWARE RANKING ALGORITHMS 92 5.5 THE FINAL TIME-AWARE
ALGORITLMI: TA3 94 5.5.1 USING A CLUSTERING TECHNIQUE 94 5.5.2 RANKING
THE EVENTS 95 5.0 THE ANATOMY OF COMETOMYHEAD. THE NEWS SEARCH ENGINE 95
5.7 EXPERIMENTAL RESULTS 98 5.7.1 EVALUATION OF THE RANKING ALGORITHMS
98 5.8 CONCLUSIONS 103 6 A METHODOLOGY FOR FAST PAGERANK COMPUTATION 105
6.1 OUR CONTRIBUTION 105 6.2 A SPARSE LINEAR SYSTEM FORMULATION 107
6.2.1 THE CONDITIONING OF THE PROBLEM IN THE NEW FORMULATION 109 6.3
EXPLOITING THE WEB MATRIX PERMUTATIONS 110 6.4 EXPERIMENTAL RESULTS 114
6.4.1 EVALUATING THE RANKING ALGORITHMS 115 6.5 CONCLUSIONS 117 7
CONCLUSIONS AND FUTURE WORKS 121 7.1 DISCUSSION ABOUT CLUSTERING AND
RANKING 121 7.2 A SYNTHESIS OF OUR CONTRIBUTIONS 122 7.3 FUTURE LINES OF
RESEARCH 123
|
| adam_txt |
CONTENTS INTRODUCTION 9 AN OVERVIEW OF WEB INFORMATION RETRIEVAL 15 1.1
AN OVERVIEW OF TRADITIONAL IR 15 1.2 MODERN WEBLR IT 1.3 SIZE OF THE WEB
1!) 1.3.1 ESTIMATING SIZES AND OVERLAPS OF SEARCH ENGINES 1!) 1.3.2
ESTIMATING THE INDEXABLE WEB 21 AN OVERVIEW OF WEB SNIPPET CLUSTERING 23
2.1 BASIC DEFINITION AND NOTATION 24 2.1.1 SIMILARITY AND DISSIMILARITY
24 2.1.2 PARTITION APPROACHES 24 2.1.3 GEOMETRIC APPROACHES 26 2.1.4
SYNTACTICAL CLUSTERING 27 2.1.5 PROBABILISTIC APPROACHES AND OTHER
METHODS 28 2.1.6 CLUSTERING REQUIREMENTS AND LIMITATIONS 28 2.2 WEB
SNIPPET CLUSTERING 29 2.2.1 THE COMMERCIAL SCENARIO 2!) 2.2.2 THE
SCIENTIFIC SCENARIO 30 2.2.3 A DEEP OVERVIEW OF THE MOST IMPORTANT
CLUSTERING ALGORITHMS 32 AN OVERVIEW OF WEB RANKING 41 3.1 BASIC
DEFINITION AND NOTATION 41 3.2 THE MATHEMATIC BEHIND TRADITIONAL WEB
RANKING 42 3.2.1 PAGERANK 44 3.2.2 HITS 45 3.2.3 SALSA 47 3.3
PERSONALIZED WEB RANKING 48 3.4 EXTENSIONS TO THE TRADITIONAL WEB
METRICS 50 3.5 ACCELERATING THE COMPUTATION OF PAGERANK 50 3.6 RANKING
ALGORITHMS BASED ON QUERY LEARNING 55 SNAKET: SNIPPET AGGREGATION FOR
KNOWLEDGE EXTRACTION 57 4.1 OUR CONTRIBUTION 58 4.2 THE ANATOMY OF
SNAKET. THE CLUSTERING ENGINE 60 4.2.1 TWO KNOWLEDGE BASES 61 4.2.2 THE
FIRST MODULE: THE SNIPPET ANALYZER 64 4.2. BIBLIOGRAFISCHE INFORMATIONEN
HTTP://D-NB.INFO/987753223 DIGITALISIERT DURCH BIBLIOGRAPHY 125 4.4
EXPERIMENTAL RESULTS 76 4.4.1 USERS SURVEYS 77 4.4.2 SNAKET'S DATASET
AND ANECDOTAL EVIDENCE 77 4.4.3 EVALUATION OF SNAKET 78 4.5 CONCLUSIONS
81 5 RANKING A STREAM OF NEWS 85 5.1 OUR CONTRIBUTION 87 5.2 STIME
DESIDERATA 88 5.3 A MODEL FOR NEWS ARTICLES 89 5.4 ALGORITHMS FOR NEWS
ARTICLES AND NEWS SOURCES 90 5.4.1 TIME-UNAWARE RANKING ALGORITHMS 91
5.4.2 TIME-AWARE RANKING ALGORITHMS 92 5.5 THE FINAL TIME-AWARE
ALGORITLMI: TA3 94 5.5.1 USING A CLUSTERING TECHNIQUE 94 5.5.2 RANKING
THE EVENTS 95 5.0 THE ANATOMY OF COMETOMYHEAD. THE NEWS SEARCH ENGINE 95
5.7 EXPERIMENTAL RESULTS 98 5.7.1 EVALUATION OF THE RANKING ALGORITHMS
98 5.8 CONCLUSIONS 103 6 A METHODOLOGY FOR FAST PAGERANK COMPUTATION 105
6.1 OUR CONTRIBUTION 105 6.2 A SPARSE LINEAR SYSTEM FORMULATION 107
6.2.1 THE CONDITIONING OF THE PROBLEM IN THE NEW FORMULATION 109 6.3
EXPLOITING THE WEB MATRIX PERMUTATIONS 110 6.4 EXPERIMENTAL RESULTS 114
6.4.1 EVALUATING THE RANKING ALGORITHMS 115 6.5 CONCLUSIONS 117 7
CONCLUSIONS AND FUTURE WORKS 121 7.1 DISCUSSION ABOUT CLUSTERING AND
RANKING 121 7.2 A SYNTHESIS OF OUR CONTRIBUTIONS 122 7.3 FUTURE LINES OF
RESEARCH 123 |
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| index_date | 2024-07-02T20:34:40Z |
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| institution | BVB |
| isbn | 9783836456579 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016452930 |
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| physical | 137 S. graph. Darst. |
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| publisher | VDM Verl., Müller |
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| spelling | Gullì, Antonio 1971- Verfasser (DE-588)135634164 aut Clustering and ranking for web information retrieval methodologies for searching the web Antonio Gullî Saarbrücken VDM Verl., Müller 2008 137 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Teilw. zugl.: Pisa, Univ., Diss., 2006 Information Retrieval swd World Wide Web swd Mathematisches Modell Document clustering Information retrieval Mathematical models Internet searching Mathematical models Web sites Ratings and rankings Mathematical models (DE-588)4113937-9 Hochschulschrift gnd-content DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016452930&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gullì, Antonio 1971- Clustering and ranking for web information retrieval methodologies for searching the web Information Retrieval swd World Wide Web swd Mathematisches Modell Document clustering Information retrieval Mathematical models Internet searching Mathematical models Web sites Ratings and rankings Mathematical models |
| subject_GND | (DE-588)4113937-9 |
| title | Clustering and ranking for web information retrieval methodologies for searching the web |
| title_auth | Clustering and ranking for web information retrieval methodologies for searching the web |
| title_exact_search | Clustering and ranking for web information retrieval methodologies for searching the web |
| title_exact_search_txtP | Clustering and ranking for web information retrieval methodologies for searching the web |
| title_full | Clustering and ranking for web information retrieval methodologies for searching the web Antonio Gullî |
| title_fullStr | Clustering and ranking for web information retrieval methodologies for searching the web Antonio Gullî |
| title_full_unstemmed | Clustering and ranking for web information retrieval methodologies for searching the web Antonio Gullî |
| title_short | Clustering and ranking for web information retrieval |
| title_sort | clustering and ranking for web information retrieval methodologies for searching the web |
| title_sub | methodologies for searching the web |
| topic | Information Retrieval swd World Wide Web swd Mathematisches Modell Document clustering Information retrieval Mathematical models Internet searching Mathematical models Web sites Ratings and rankings Mathematical models |
| topic_facet | Information Retrieval World Wide Web Mathematisches Modell Document clustering Information retrieval Mathematical models Internet searching Mathematical models Web sites Ratings and rankings Mathematical models Hochschulschrift |
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