Augmenting Dublin Core digital library metadata with Dewey Decimal Classification

Khoo, Michael; Ahn, Jae‐wook; Binding, Ceri; Jones, Hilary; Lin, Xia; Massam, Diana; Tudhope, Douglas

doi:10.1108/jd-07-2014-0103

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Joorabchi and Mahdi (2014) explored the possibility to improve automated DDC classification based on external resources: references from the document to be classified and Wikipedia. Khoo et al (2015) created DDC terms and numbers from pre-existing Dublin Core metadata.…”

Section: Related Researchmentioning

confidence: 99%

Automated Dewey Decimal Classification of Swedish library metadata using Annif software

Golub,

Suominen,

Mohammed

et al. 2024

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PurposeIn order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.Design/methodology/approachOn a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.FindingsThe best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.Originality/valueThe paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

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Section: Related Researchmentioning

confidence: 99%

Automated Dewey Decimal Classification of Swedish library metadata using Annif software

Golub,

Suominen,

Mohammed

et al. 2024

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“…Классификация научных текстов в соответствии с УДК основывается на ключевых словах, содержащихся в тексте [5]. Точно так же библиографические метаданные, такие как заголовок, описание и тематические теги, могут использоваться для дополнения библиографических записей публикации десятичной классификацией Дью (Dewey Decimal Classification, DDC) [6]. Распространение цифровых ресурсов и их интеграции в традиционную библиотечную среду создали потребность в автоматизированном инструменте для определения тематики публикации в соответствии со схемами библиотечной классификации.…”

Section: смежные работыunclassified

Service for assigning a UDC code to mathematical articles based on semantic technologies

Almukhametov¹,

Nevzorova²

2022

Proceedings of 24th Scientific Conference “Scientific Services &Amp; Internet – 2022”

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Classification of documents with the assignment of classifier codes is a traditional way of systematizing and searching for documents on a specific topic. The Universal Decimal Classification (UDC) underlies the systematization of knowledge presented in libraries, databases and other information repositories. In Russia, UDC is an obligatory attribute of all book production and information on natural and technical sciences. The choice of classification codes is associated with the analysis of the structure of the classifier tree and is traditionally decided by the author of a scientific article. This article proposes a solution for automating the assigning the UDC classification code for a mathematical article based on a special resource - the OntoMathPro ontology for professional mathematics, developed at Kazan Federal University. An approach to solving the problem is to create "code maps" for each classifying code in the UDC tree in the field of mathematics. Under the "code map" is meant a weighted set of all extracted, with the help of OntoMathPro ontology, mathematical named entities from the collection of articles with a given UDC code. The creation of "code maps" is based on the hypothesis that the choice of the UDC code is determined by a certain set of classifying features that can be represented by classes from the OntoMathPro ontology. The proposed hypothesis was tested and confirmed in the paper. The hypothesis was tested on a collection of mathematical articles An approach to solving the problem is to create "code maps" for each classifying code in the UDC tree in the field of mathematics. Under the "code map" is meant a weighted set of all extracted, with the help of OntoMathPro ontology, mathematical named entities from the collection of articles with a given UDC code. The creation of "code maps" is based on the hypothesis that the choice of the UDC code is determined by a certain set of classifying features that can be represented by classes from the OntoMathPro ontology. The proposed hypothesis was tested and confirmed in the paper. The hypothesis was tested on a collection of mathematical articles published during 1999-2009 in the "Izvestiya VUZov. Mathematics" journal.

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“…Regarding the classification of scholarly texts according to the UDC (Romanov et al , 2016), texts are classified by peers based on their keywords. Similarly, bibliographic metadata (title, description and subject tags) can be used to equip texts with DDC to supplement bibliographic records of publications (Khoo et al , 2015). Also, for e-news, support can be developed for automatic inclusion into pre-defined groups, such as art, function, news, reviews, sports, world (Asy'arie and Pribadi, 2009; Ramdass and Seshasai, 2009).…”

Section: Related Workmentioning

confidence: 99%

Automatic classification of older electronic texts into the Universal Decimal Classification–UDC

Kragelj

Borštnar

2020

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PurposeThe purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.Design/methodology/approachThe general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.FindingsResults suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.Research limitations/implicationsThe main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.Practical implicationsThe classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.Social implicationsThe proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.Originality/valueThese findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.

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Augmenting Dublin Core digital library metadata with Dewey Decimal Classification

Cited by 7 publications

References 22 publications

Automated Dewey Decimal Classification of Swedish library metadata using Annif software

Automated Dewey Decimal Classification of Swedish library metadata using Annif software

Service for assigning a UDC code to mathematical articles based on semantic technologies

Automatic classification of older electronic texts into the Universal Decimal Classification–UDC

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