Automatically assessing resource quality for educational digital libraries

Wetzler, Philipp; Bethard, Steven; Butcher, Kirsten R.; Martin, James H.; Sumner, Tamara

doi:10.1145/1526993.1526997

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Bypassing librarians 4 Ewing & Hauptman, 1995;Gorichanaz et al, 2020;Nolin, 2013) No role 47 (e.g., Stehno & Retti, 2003;Wetzler et al, 2009) RQ 3 Role of non-humans AI-1 Tool/system 51 (e.g., Alexander et al, 2019;Baba et al, 2016;Iqbal et al, 2020;Jadhav & Shenoy, 2020;Kanarkard et al, 2017;Rubin et al, 2010;Schoeb et al, 2020;Tsuji et al, 2014 Benedetti et al, 2020;Finnemann, 2014;Lorang et al, 2020;Muehlberger et al, 2019;Nolin, 2013;Sidorko, 2009) AI-6…”

Section: Appendix A: Table Of Codesmentioning

confidence: 99%

Understanding Artificial Intelligence in Research Libraries – Extensive Literature Review

Gasparini

Kautonen

2022

LIBER

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Artificial intelligence (AI) now forms a part of various activities in the academic world. AI will also affect how research libraries perform and carry out their services and how the various kinds of data they hold in their repositories will be used in the future. For the moment, the landscape is complex and unclear, and library personnel and leaders are uncertain about where they should lay the path ahead. This extensive literature review provides an overview of how research libraries understand, react to, and work with AI. This paper examines the roles conceived for libraries and librarians, their users, and AI. Finally, design thinking is presented as an approach to solving emerging issues with AI and opening up opportunities for this technology at a more strategic level.

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Section: Appendix A: Table Of Codesmentioning

confidence: 99%

Understanding Artificial Intelligence in Research Libraries – Extensive Literature Review

Gasparini

Kautonen

2022

LIBER

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“…To apply machine learning (ML) to one of the standard DL circulation activities, namely text categorization [48], is part of the cognitive toolbox deployed [18]. In this context, ML is extensively being experimented with in different development areas and scenarios; to name but a few, for extracting image content from figures in scientific documents for categorization [33,34], automatically assessing and characterizing resource quality for educational DL [54,5], assessing the quality of scientific conferences [37], web-based collection development [42], automated document metadata extraction by support vector machines (SVM, [24]), automatic extraction of titles from general documents [27], information architecture [17], to remove duplicate documents [9], for collaborative filtering [59], for the automatic expansion of domain-specific lexicons by term categorization [3], for generating visual thesauri [45], or the semantic markup of documents [13]. As part of this direction of research, ML is being tested for its ability to reproduce parts of collections indexed by widespread classification schemes in a supervised learning setting, such as automatic text categorization using the Dewey Decimal Classification (DDC, [52]), or the Library of Congress Classification (LCC) from Library of Congress Subject Headings (LCSH, [20,43]).…”

Section: Introductionmentioning

confidence: 99%

Using wavelet analysis for text categorization in digital libraries: a first experiment with Strathprints

2012

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Digital libraries increasingly benefit from research on automated text categorization for improved access. Such research is typically carried out by using standard test collections. In this paper we present a pilot experiment of replacing such test collections by a set of 6000 objects from a real-world digital repository, indexed by Library of Congress Subject Headings, and test support vector machines in a supervised learning setting for their ability to reproduce the existing classification. To augment the standard approach, we introduce a combination of two novel elements: using functions for document content representation in Hilbert space, and adding extra semantics from lexical resources to the representation. Results suggest that wavelet-based kernels slightly outperformed traditional kernels on classification reconstruction from abstracts and vice versa from full-text documents, the latter outcome due to word sense ambiguity. The practical implementation of our methodological framework enhances the analysis and representation of specific knowledge relevant to large-scale digital collections, in this case the thematic coverage of the collections. Representation of specific knowledge about digital collections is one of the basic elements of the persistent archives and the less studied one (compared to representations of digital objects and collections). Our research is an initial step in this direction developing further the methodological approach and demonstrating that text categorisation can be applied to analyse the thematic coverage in digital repositories.

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