Bibliomining for automated collection development in a digital library setting: Using data mining to discover Web‐based scholarly research works

Nicholson, Scott

doi:10.1002/asi.10313

Cited by 20 publications

(14 citation statements)

References 23 publications

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“…Research is pointing the way to some machine‐assisted acquisition or content development mechanisms (Nicholson, 2003). But the roles of collection development staff (Dorner, 2004) are related to organizational policy and participation in a variety of different types of consortia.…”

Section: Servicesmentioning

confidence: 99%

Digital libraries

Bearman¹

2007

Annual Review Info Sci & Tec

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Section: Servicesmentioning

confidence: 99%

Digital libraries

Bearman¹

2007

Annual Review Info Sci & Tec

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“…A growing body of literature (Agnew, 2008;Ardito et al, 2006;Bishoff and Allen, 2004;Blandford and Buchanan, 2003;Clements and Pawlowski, 2012;Hall, Naughton, and Lin, 2009;Kimbrough, Padmanabhan, and Zheng, 2000;Lim and Ko, 2005;Nicholson, 2003;NISO Framework Working Group, 2007;Saracevic, 2005;Xie, 2006;Xie, 2008) acknowledges the importance of the following:…”

Section: Collectionsmentioning

confidence: 99%

Digital Resources for STEM Educators and Recommendations for Cyberlearning Initiatives: Results from the National Science Foundation Digital Library/Distributed Learning Program Evaluation

Bikson¹,

Straus²,

Agnew³

et al. 2017

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“…This could mean applying a better user study methodology that would help participants make more homogeneous decisions. Another way would be to harvest web pages that are more uniform in terms of quality or genre (see, for example, Custard and Sumner 2005;Nicholson 2003), or to design more narrowly specified search tasks for a certain purpose. Appendix 1.…”

Section: Paper V the Role Of Different Thesaurus Terms And Captions mentioning

confidence: 99%

Automated Subject Classification of Textual Documents in the Context of Web-Based Hierarchical Browsing

Golub¹

2011

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Background 2.1 Terminology 2.2 Approaches to automated classification (II) 2.3 Evaluation challenge 2.4 Hierarchical subject browsing 3 Methodology 3.1 User studies and performance measures 3.2 Document collections 3.3 Engineering Information thesaurus and classification scheme 3.4 The string-matching classification algorithm 4 Results 4.1 Usage of web-based browsing (I, VIII) 4.2 Suitability of DDC and Ei for hierarchical web-based browsing (I, VIII) 4.3 Improving the string-matching algorithm (III, IV, V, VI) 4.4 The string-matching algorithm on an abstracts collection (VI) 4.5 String-matching algorithm on a web-page collection (VIII) 5 Concluding remarks References viii Contents 1 Introduction 2 Approaches to automated classification 2.1 Text categorization 2.2 Document clustering 2.3 Document classification 2.4 Mixed approach 3 Discussion 3.1 Features of automated classification approaches 3.2 Evaluation 3.3 Application for subject browsing x Contents 4.5 Evaluation 5 Conclusion Acknowledgments References V. The Role of Different Thesaurus Terms and Captions in Automated Subject Classification 1 Introduction 2 Methodology 2.1 String-matching algorithm 2.2 Document collection 2.3 Term lists 2.4 Stop-word list and stemming 2.5 Evaluation methodology 3 Experimental results 3.1 Averaged results for all the classes 3.2 Individual classes 4 Concluding remarks Acknowledgments References VI. Automated Classification of Textual Documents Based on a Controlled Vocabulary in Engineering 1 Introduction 2 Methodology 2.1 String-matching algorithm 2.2 Document collection 2.3 Evaluation methodology 3 Improving the algorithm 3.1 Term weights 3.2 Cutoffs 3.3 Enriching the term list with new terms 4 Results 4.1 Weights and cutoffs 4.2 Enhancing the term list with new terms Contents xi 4.3 Terms analysis and shortened term lists 5 Conclusion Acknowledgments References VII. Comparing and Combining Two Approaches to Automated Subject Classification of Text 187 1 Introduction 2 Methodology 2.1 String-matching algorithm 2.2 Linear support vector machines (SVM) algorithm 2.3 Document collection 2.4 Evaluation 2.5 Term lists (features) 3 Results 3.1 Comparison in original settings 3.2 Combining term lists (features) 4 Conclusion Acknowledgments References VIII. Automated Subject Classification of Engineering Web Pages in Hierarchical Browsing: A User Study 207 4 Results 4.1 Browsing 4.2 Automatically assigned classes 5 Conclusions xii Contents Acknowledgments 240 References 240 Appendix 1. Hierarchical tree for the class 9 (Engineering, General) of the Engineering Information classification scheme. 243 Appendix 2. Pre-experiment questionnaire. 247 Appendix 3. Post-experiment questionnaire. 249 Appendix 4. Hierarchical view of an ideal sequence of browsing steps for each task. 251 Appendix 5. Tasks rotation scheme. 253 Appendix 6. Instructions sheet. 255 Appendix 7. Main user study screen. 259

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Bibliomining for automated collection development in a digital library setting: Using data mining to discover Web‐based scholarly research works

Cited by 20 publications

References 23 publications

Digital libraries

Digital libraries

Digital Resources for STEM Educators and Recommendations for Cyberlearning Initiatives: Results from the National Science Foundation Digital Library/Distributed Learning Program Evaluation

Automated Subject Classification of Textual Documents in the Context of Web-Based Hierarchical Browsing

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