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1

Green, Rebecca. "See-also relationships in the Dewey Decimal Classification." NASKO 3, no. 1 (November 2, 2011): 41. http://dx.doi.org/10.7152/nasko.v3i1.12789.

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This paper investigates the semantics of topical, associative see-also relationships in schedule and table entries of the Dewey Decimal Classification (DDC) system. Based on the see-also relationships in a random sample of 100 classes containing one or more of these relationships, a semi-structured inventory of sources of see-also relationships is generated, of which the most important are lexical similarity, complementarity, facet difference, and relational configuration difference. The premise that see-also relationships based on lexical similarity may be language-specific is briefly examined. The paper concludes with recommendations on the continued use of see-also relationships in the DDC.
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Mohamad, Rotmianto. "Observing Optional Number in DDC Edition 23." Record and Library Journal 1, no. 1 (January 2, 2018): 48. http://dx.doi.org/10.20473/rlj.v1-i1.2015.48-58.

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Dewey Decimal Classification is a most popular classification system in the world because of its completeness and most up-to-date. There are many optional number in this classification system, although it rarely to be discussed even it is important to known well about that optional number, especially for a librarian as classifier. This paper is a literature study about Dewey Decimal Classification Edition 23, to describe about optional numbers, particularly the number in relationship with Indonesia’s subject and discipline. This paper is to avoid misunderstanding in interpreted about optional number among librarians, especially for who that does not understand well about optional numbers.
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Rotmianto, Mohamad. "Observing Optional Number in DDC Edition 23." Record and Library Journal 1, no. 1 (April 29, 2015): 48. http://dx.doi.org/10.20473/rlj.v1i1.78.

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Dewey Decimal Classification is a most popular classification system in the world because of its completeness and most up-to-date. There are many optional number in this classification system, although it rarely to be discussed even it is important to known well about that optional number, especially for a librarian as classifier. This paper is a literature study about Dewey Decimal Classification Edition 23, to describe about optional numbers, particularly the number in relationship with Indonesia’s subject and discipline. This paper is to avoid misunderstanding in interpreted about optional number among librarians, especially for who that does not understand well about optional numbers.
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Merkley, Cari. "The Library of Congress, Dewey Decimal, and Universal Decimal Classification Systems are Incomplete and Unsystematic." Evidence Based Library and Information Practice 6, no. 4 (December 15, 2011): 134. http://dx.doi.org/10.18438/b8qk7s.

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Objective – To determine the extent to which knowledge is currently addressed by the Library of Congress (LCC), Dewey Decimal (DDC), and Universal Decimal (UDC) classification systems. Design – Comparative analysis of the LCC, DDC, and UDC systems using Zin’s 10 Pillars of Knowledge. Setting – The Faculty of Philosophy and Science at a Brazilian university. Subjects – Forty one subject-related classes and 386 subclasses from the first two levels of the LCC, DDC, and UDC systems. Methods – To evaluate the LCC, DDC, and UDC systems, the researchers employed the 10 Pillars of Knowledge, a “hierarchical knowledge tree” developed by the lead author of this study (p. 878). According to the authors, the 10 Pillars of Knowledge seek to illustrate relationships between fields of knowledge while capturing their breadth. The first level of the Pillars consists of the following categories: Knowledge, Supernatural, Matter and Energy, Space and Earth, Nonhuman Organizations, Body and Mind, Society, Thought and Art, Technology, and History. Each of the 10 Pillars is further subdivided, resulting in a four level hierarchical structure of 76 categories. Of the 76 categories, 55 are unique subject areas. A selection of subject-based classes and subclasses from the first two levels of the LCC, DDC, and UDC systems were then mapped to the relevant subclasses within the Pillars. Analysis was limited to the first two levels of LCC, DDC, and UDC, except for the LCC categories of BF and BL where further subclasses were analyzed. Classes or subclasses in LCC, DDC, or UDC that were not subject based (for example, those based on publication type) were excluded from the study. In total, 41 main classes and 386 subclasses from LLC, DDC, and UDC were categorized using the 10 Pillars. Main Results – The LLC, DDC, and UDC systems were deemed to be complete and systematic in their coverage of only three of the 10 Pillars: Matter and Energy, Thought and Art, and History. This means that there was at least one class or subclass in each of the three systems that corresponded to the subclasses in these pillars. The remaining seven pillars were only partially covered by the three systems to varying degrees. For example, the coverage of religion in LCC and DDC show evidence of a bias towards Christianity and incomplete coverage of other faiths. In addition to the lack of completeness in terms of subject coverage, the researchers found inconsistencies and problems with how relationships between subjects were illustrated by the systems. For example, botany should be a subclass of biology, but the subjects occupy the same level in the LCC, DDC, and UDC systems. Researchers also noted cases where subclasses on the same level were not mutually exclusive e.g., the BR (Christianity) and BS (The Bible) subclasses in LCC. Overall, LLC performed slightly better than DDC or UDC, covering 47 of the 55 unique subject categories in the 10 Pillars. It was followed by UDC with 44 out of 55, and DDC with 43 out of 55. Some of the 55 unique subject categories in the 10 Pillars system were not represented by any of the systems: 3 subclasses under Society (Society at Large – Area Based, Social Groups – Age, and Social Groups – Ethnicity), 2 under Technology (Technologies – Materials and Technologies – Processes), and 1 under Foundations (Methodology). Conclusion – The researchers conclude that none of the three major classification systems analyzed provides complete and systematic coverage of the world of knowledge, and call for the library community to move to new systems, such as the 10 Pillars of Knowledge.
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Patterson, Jordan. "Dewey Decimal Classification Trending Downward in U.S. Academic Libraries, but Unlikely to Disappear Completely." Evidence Based Library and Information Practice 14, no. 3 (September 12, 2019): 156–58. http://dx.doi.org/10.18438/eblip29592.

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A Review of: Lund, B., & Agbaji, D. (2018). Use of Dewey Decimal Classification by academic libraries in the United States. Cataloging and Classification Quarterly, 56(7), 653-661. https://doi.org/10.1080/01639374.2018.1517851 Abstract Objective – To determine the current use of Dewey Decimal Classification in academic libraries in the United States of America (U.S.). Design – Cross-sectional survey using a systematic sampling method. Setting – Online academic library catalogues in the U.S. Subjects – 3,973 academic library catalogues. Methods – The researchers identified 3,973 academic libraries affiliated with degree-granting post-secondary institutions in the U.S. The researchers searched each library’s online catalogue for 10 terms from a predetermined list. From the results of each search, the researchers selected at least five titles, noted the classification scheme used to classify each title, and coded the library as using Dewey Decimal Classification (DDC), Library of Congress Classification (LCC), both DDC and LCC, or other classification schemes. Based on the results of their data collection, the researchers calculated totals. The totals of this current study’s data collection were compared to statistics on DDC usage from two previous reports, one published in 1975 and one in 1996. The researchers performed statistical analyses to determine if there were any discernible trends from the earliest reported statistics through to the current study. Main Results – Collections classified using DDC were present in 717 libraries (18.9%). Adjusting for the increase in the number of academic libraries in the U.S. between 1975 and 2017, DDC usage in academic libraries has declined by 56% in that time frame. The number of libraries with only DDC in evidence is unreported. Conclusion – The previous four decades have seen a significant decrease in the use of DDC in U.S. academic libraries in favour of LCC; however, the rate at which DDC has disappeared from academic libraries has slowed dramatically since the 1960s. There is no clear indication that DDC will disappear from academic libraries completely.
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Golub, Koraljka, Johan Hagelbäck, and Anders Ardö. "Automatic Classification of Swedish Metadata Using Dewey Decimal Classification: A Comparison of Approaches." Journal of Data and Information Science 5, no. 1 (April 22, 2020): 18–38. http://dx.doi.org/10.2478/jdis-2020-0003.

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AbstractPurposeWith more and more digital collections of various information resources becoming available, also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems. While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification (DDC) classes for Swedish digital collections, the paper aims to evaluate the performance of six machine learning algorithms as well as a string-matching algorithm based on characteristics of DDC.Design/methodology/approachState-of-the-art machine learning algorithms require at least 1,000 training examples per class. The complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of DDC in order to provide sufficient training data (totaling 802 classes in the training and testing sample, out of 14,413 classes at all levels).FindingsEvaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average; the string-matching algorithm outperforms machine learning for specific classes when characteristics of DDC are most suitable for the task. Word embeddings combined with different types of neural networks (simple linear network, standard neural network, 1D convolutional neural network, and recurrent neural network) produced worse results than Support Vector Machine, but reach close results, with the benefit of a smaller representation size. Impact of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as input. Stemming only marginally improves the results. Removed stop-words reduced accuracy in most cases, while removing less frequent words increased it marginally. The greatest impact is produced by the number of training examples: 81.90% accuracy on the training set is achieved when at least 1,000 records per class are available in the training set, and 66.13% when too few records (often less than 100 per class) on which to train are available—and these hold only for top 3 hierarchical levels (803 instead of 14,413 classes).Research limitationsHaving to reduce the number of hierarchical levels to top three levels of DDC because of the lack of training data for all classes, skews the results so that they work in experimental conditions but barely for end users in operational retrieval systems.Practical implicationsIn conclusion, for operative information retrieval systems applying purely automatic DDC does not work, either using machine learning (because of the lack of training data for the large number of DDC classes) or using string-matching algorithm (because DDC characteristics perform well for automatic classification only in a small number of classes). Over time, more training examples may become available, and DDC may be enriched with synonyms in order to enhance accuracy of automatic classification which may also benefit information retrieval performance based on DDC. In order for quality information services to reach the objective of highest possible precision and recall, automatic classification should never be implemented on its own; instead, machine-aided indexing that combines the efficiency of automatic suggestions with quality of human decisions at the final stage should be the way for the future.Originality/valueThe study explored machine learning on a large classification system of over 14,000 classes which is used in operational information retrieval systems. Due to lack of sufficient training data across the entire set of classes, an approach complementing machine learning, that of string matching, was applied. This combination should be explored further since it provides the potential for real-life applications with large target classification systems.
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Khoo, Michael John, Jae-wook Ahn, Ceri Binding, Hilary Jane Jones, Xia Lin, Diana Massam, and Douglas Tudhope. "Augmenting Dublin Core digital library metadata with Dewey Decimal Classification." Journal of Documentation 71, no. 5 (September 14, 2015): 976–98. http://dx.doi.org/10.1108/jd-07-2014-0103.

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Purpose – The purpose of this paper is to describe a new approach to a well-known problem for digital libraries, how to search across multiple unrelated libraries with a single query. Design/methodology/approach – The approach involves creating new Dewey Decimal Classification terms and numbers from existing Dublin Core records. In total, 263,550 records were harvested from three digital libraries. Weighted key terms were extracted from the title, description and subject fields of each record. Ranked DDC classes were automatically generated from these key terms by considering DDC hierarchies via a series of filtering and aggregation stages. A mean reciprocal ranking evaluation compared a sample of 49 generated classes against DDC classes created by a trained librarian for the same records. Findings – The best results combined weighted key terms from the title, description and subject fields. Performance declines with increased specificity of DDC level. The results compare favorably with similar studies. Research limitations/implications – The metadata harvest required manual intervention and the evaluation was resource intensive. Future research will look at evaluation methodologies that take account of issues of consistency and ecological validity. Practical implications – The method does not require training data and is easily scalable. The pipeline can be customized for individual use cases, for example, recall or precision enhancing. Social implications – The approach can provide centralized access to information from multiple domains currently provided by individual digital libraries. Originality/value – The approach addresses metadata normalization in the context of web resources. The automatic classification approach accounts for matches within hierarchies, aggregating lower level matches to broader parents and thus approximates the practices of a human cataloger.
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Landry, Patrice. "Dewey Decimal Classification (DDC) at the Swiss National Library." Cataloging & Classification Quarterly 42, no. 3-4 (August 2006): 135–45. http://dx.doi.org/10.1300/j104v42n03_05.

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9

Green, Rebecca, and Michael Panzer. "The Interplay of Big Data, WorldCat, and Dewey." Advances in Classification Research Online 24, no. 1 (January 9, 2014): 51. http://dx.doi.org/10.7152/acro.v24i1.14677.

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As the premier example of big data in the bibliographic world, WorldCat has the potential to support knowledge discovery in many arenas. After giving evidence for a big data characterization of WorldCat, the paper explores this knowledge discovery potential from two perspectives related to the Dewey Decimal Classification (DDC) system: (1) how WorldCat data can inform development of the DDC (classification analytics) and (2) how DDC-classified content in WorldCat can shed light on the bibliographic world itself (collection analytics). In the realm of classification analytics, WorldCat data support decisions to modify the DDC by expanding or reducing the number of classes, adding topical coverage, or adding subject access points; data analysis can support recognition of (1) trending topics and (2) the faceted structure of subject domains. In the realm of collection analytics, the paper considers as possible applications the use of the DDC in the topical "fingerprinting" of categorized content in WorldCat or in performing a bibliographic gap analysis.
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Kim, Jeong-Hyen, and Ji-Hyun Moon. "A Study on the Development of the Abridged Dewey Decimal Classification." Journal of the Korean Society for Library and Information Science 43, no. 3 (September 30, 2009): 379–97. http://dx.doi.org/10.4275/kslis.2009.43.3.379.

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11

Golub, Koraljka, Marianne Lykke, and Douglas Tudhope. "Enhancing social tagging with automated keywords from the Dewey Decimal Classification." Journal of Documentation 70, no. 5 (September 2, 2014): 801–28. http://dx.doi.org/10.1108/jd-05-2013-0056.

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Purpose – The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval. Design/methodology/approach – Over 11,000 Intute metadata records in politics were used. Totally, 28 politics students were each given four tasks, in which a total of 60 resources were tagged in two different configurations, one with uncontrolled social tags only and another with uncontrolled social tags as well as suggestions from a controlled vocabulary. The controlled vocabulary was DDC comprising also mappings from the Library of Congress Subject Headings. Findings – The results demonstrate the importance of controlled vocabulary suggestions for indexing and retrieval: to help produce ideas of which tags to use, to make it easier to find focus for the tagging, to ensure consistency and to increase the number of access points in retrieval. The value and usefulness of the suggestions proved to be dependent on the quality of the suggestions, both as to conceptual relevance to the user and as to appropriateness of the terminology. Originality/value – No research has investigated the enhancement of social tagging with suggestions from the DDC, an established KOS, in a user trial, comparing social tagging only and social tagging enhanced with the suggestions. This paper is a final reflection on all aspects of the study.
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Tjiparuro, Zeundjua, and Shadreck Mumbiana Situmbeko. "Managing Hardcopy Drawings." International Journal of Engineering Research in Africa 7 (September 2012): 41–53. http://dx.doi.org/10.4028/www.scientific.net/jera.7.41.

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This Paper Presents a Classification System for Managing and Indexing Hardcopy Engineering Drawings, Referred to, in this Paper, as Drawings Library Management System (DLMS), Developed for an Appropriate Technology Research Centre in Botswana. the System Is Akin to Common Library Cataloguing and Classification Systems such as the Dewey Decimal Classification (DDC), Universal Decimal Classification and Others, which, though Are Well-Known and Have Been around for many Years, Are yet to Be Exploited for the Classification and Management of Paper Drawings. in Fact, Hardcopy Drawings Appear to Be Neglected Compared to the Softcopy Type, for which Various Scholarly Articles Have Been Done on their Classification, Retrieval and Management. this Is Unfortunate as, Invariably, it Is Hardcopy/paper Drawings that Are Mostly Used in Production, Especially in Developing Countries where the Power of CAD/CAM Is yet to Be Fully Harnessed. Accordingly, the DLMS, a Model Successfully Used in a Research Centre in Botswana for over Ten Years Is Proposed.The System Was Further Analysed against and Compared to Three Main Library Classification Systems, Namely, the Dewey Decimal, the Universal Decimal and the Library of Congress Systems. Results Show that the DLMS Is a Better Classification System for Managing Hardcopy Drawings.
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Rohman, Asep Saeful, Prijana Prijana, and Samson CMS. "Perluasan notasi Dewey Decimal Classification (DDC) tentang bahasa dan susastra Sunda." Jurnal Kajian Informasi dan Perpustakaan 5, no. 2 (May 31, 2018): 155. http://dx.doi.org/10.24198/jkip.v5i2.11014.

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Green, Rebecca, and Giles Martin. "A Rosid Is a Rosid Is a Rosid . . . or Not." Advances in Classification Research Online 23, no. 1 (January 30, 2013): 9. http://dx.doi.org/10.7152/acro.v23i1.14228.

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The current structure of 583 Magnoliopsida (Dicotyledons) and 584 Liliopsida (Monocotyledons) in the Dewey Decimal Classification (DDC) system reflects changes made when the life sciences were thoroughly revised in 1996. Since that time, considerable progress has been made in the phylogenetic classification of angiosperms (flowering plants). In particular, APG III, the 2009 version of the classification developed by the Angiosperm Phylogeny Group, is finding use as a tool to organize both botanical information and botanical collections. The Dewey Editorial Office has received a request to revise 583–584 in light of this taxonomy ―as appropriate‖; relevant revisions would be likely to include both structural and terminological changes. In deciding how to provide accommodation for APG III, the Dewey editorial team must address many issues: Is APG III now stable enough and accepted broadly enough to be adopted as the basis for a major revision of the DDC? Should revisions in 583–584 be coordinated with parallel revisions in other parts of the life sciences? What revision strategies can be considered in revising 583-584 to accommodate APG III? What are their various strengths and weaknesses? How have other major classification schemes (e.g., the UDC) accommodated APG III? Discussion of these issues is guided by the principles (―editorial rules‖) that govern development of the DDC.
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Lowisch, Maximilian. "Der Übergang des schwedischen Bibliothekswesens zur DDC als Aufstellungs- und Erschließungsklassifikation." Bibliotheksdienst 53, no. 2 (January 10, 2019): 94–108. http://dx.doi.org/10.1515/bd-2019-0018.

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Zusammenfassung Der nachfolgende Aufsatz berichtet über die Einführung der Dewey Decimal Classification (DDC) an schwedischen Bibliotheken im Rahmen eines Projekts der schwedischen Nationalbibliothek (Kungliga biblioteket) während der Jahre 2009 bis 2012. Dazu wird zunächst ein kurzer Überblick über den heutigen Stand der sachlichen Erschließung an schwedischen Bibliotheken gegeben, geschichtliche Hintergründe beschrieben und kursorisch die Struktur der Klassifikation „SAB:s Klassifikationssystem för svenska bibliotek“ (SAB) sowie des Schlagwortsystems „Svenska ämnesord“ (SAO) erläutert. Hiernach werden die Projektziele der DDC-Einführung und deren Umsetzung dargestellt. Beleuchtet wird weiterhin, wieso die DDC an Wissenschaftlichen Bibliotheken in Schweden mittlerweile große Verbreitung gefunden hat, nicht jedoch an den Öffentlichen Bibliotheken.
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Rosita, Yesy Diah, and Yanuarini Nur Sukmaningtyas. "LSTM Network and OCR Performance for Classification of Decimal Dewey Classification Code." Record and Library Journal 6, no. 1 (April 13, 2020): 45. http://dx.doi.org/10.20473/rlj.v6-i1.2020.45-56.

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Background of the study: Giving book code by a librarian in accordance with the Decimal Dewey Classification system aims to facilitate the search for books on the shelf precisely and quickly. Purpose: The first step in giving code to determine the class of books is the principal division which has 10 classes.Method: This study proposed Optical Character Recognition to read the title text on the book cover, preprocessing the text, and classifying it by Long Short-Term Memory Neural Network. Findings: In general, a librarian labeled a book by reading the book title on the book cover and doing book class matching with the book guide of DDC. Automatically, the task requires time increasingly. We tried to classify the text without OCR and utilize OCR which functions to convert the text in images into text that is editable. BY the experimental result, the level of classification accuracy without utilizing OCR is higher than using OCR. Conclusion: The magnitude of the accuracy is 88.57% and 74.28% respectively. However, the participation of OCR in this classification is quite efficient enough to assist a beginner librarian to overcome this problem because the accuracy difference is less than 15%.
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Saeed, Hamid, and Abdus Sattar Chaudhry. "Using Dewey decimal classification scheme (DDC) for building taxonomies for knowledge organisation." Journal of Documentation 58, no. 5 (October 2002): 575–83. http://dx.doi.org/10.1108/00220410210441595.

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Sukiasyan, Eduard. "New Russian State Standard devoted Universal Decimal Classification." Scientific and Technical Libraries, no. 12 (December 1, 2016): 67–80. http://dx.doi.org/10.33186/1027-3689-2016-12-67-80.

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Due to certification of GOST P 7.0.90-2016 regulating structure, rules of maintaining and indexing on the Universal Decimal Classification (UDC) information of system in general, of the UDC Consortium, the place which occupies UDC in the world is given. It is told also about M. Dewey Decimal classification (DDC) and activities of International society for knowledge organization (ISKO). The structure of the standard is in detail considered, a number of correctly notes on the applied terminology indicated. Recently published guide books which can be used for profound studying of classification systems and technology of indexing are listed. At the end of article the author says about why all librarians should know UDC. Influence of this system on further development of the theory and practice of classification is huge. Article in general is aimed at the development of classification culture.
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Chung, Yeon-Kyoung. "A Study on Modification and Expansion of Dewey Decimal Classification about Immigration Policy." Journal of the Korean Society for information Management 28, no. 4 (December 30, 2011): 33–48. http://dx.doi.org/10.3743/kosim.2011.28.4.033.

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Kononova, E. V., and E. R. Sukiasyan. "Publishing classification systems on the Internet: representation and use." Scientific and Technical Libraries 1, no. 2 (April 9, 2021): 91–100. http://dx.doi.org/10.33186/1027-3689-2021-2-91-100.

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Classification systems are published primarily in the book form, for various purposes and in different versions. They are identified by the number or year of publication. The authors propose another variant of maintaining classification systems, i.e. in the card format.The authors discuss the issues of presenting classification schedules of popular classification systems on the Internet with special focus on their applications. They explain why privately owned classification systems, like Dewey Decimal Classification (DDC) and Universal Decimal Classification (UDC), are not published on the Internet, while the systems supported by government organizations (e.g. national libraries) are traditionally published openly. The authors provide the example of the Library of Congress Classification (LCC), which is the United States’ National Classification System and which is published on open access. The project of the open presentation of the Library-Bibliographical Classification (LBC) in several versions supplemented with indices on the website of the Russian State Library (RSL) is described in more detail. The possibilities for their use are analyzed.
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Choi, InKyung, and Min Sook Park. "A Cross-cultural Comparison of Medical Science Subject Classification in the KDC and the DDC." Advances in Classification Research Online 29, no. 1 (June 28, 2019): 17. http://dx.doi.org/10.7152/acro.v29i1.15457.

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This study reports the preliminary results of a cross-cultural comparison of Medical Science in the Korean Decimal Classification (KDC) and in the Dewy Decimal Classification (DDC). Despite having similar purposes, to serve the public and emphasize the significance of standardization in medical science, a comparison of the two classification systems shows the influences of social and cultural inferences of classification systems in medicine.
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Rotmianto, Mohamad, and Eko Wahyudi. "Developing Plugin e-DDC as an Additional Application for Senayan Library Management System with PHP Language Programming and MySQL Database." Record and Library Journal 2, no. 1 (July 25, 2016): 16. http://dx.doi.org/10.20473/rlj.v1i3.2124.

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Senayan Library Management System, usually called as SLiMS dan e-DDC (electronic Dewey Decimal Classification), is an application for library that now is used widely because it is easy to use, guaranteed of updating from the provider, and free. SLiMs and e-DDC is actually two separated application, but in the practice, it is possible and recommended to be used together. SLiMS is used as a tool of library automation, meanwhile e-DDC functions to help finding collection classification number. Some users of SLiMS and e-DDC have suggested to develop SLiMS that is integral with e-DDC, so the use of both application can be practical which can make the librarian easier to manage the library. Based on the suggestion, the writer and e-DDC team finally created and designed additional application which is called plugin in SLiMS automation system that contained e-DDC. The plugin was created using PHP MySQL and MySQL database. The purpose of this paper is to enrich the reference of library application development, especially that is based on Free Open Source Software (FOSS). The method used is Research and Development method. The result was the Plugin e-DDC for SLiMS was finished and released on May 2, 2015 to celebrate National Education Day.
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Ronaldo, Deddy, and Aderay Saputra. "APLIKASI PELABELAN BUKU DENGAN DECIMAL DEWEY CLASSIFICATION (DDC) PADA PERPUSTAKAAN TEKNIK INFORMATIKA UNIVERSITAS PALANGKA RAYA BERBASIS WEBSITE." Jurnal Teknologi Informasi Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika 12, no. 2 (August 1, 2018): 24–29. http://dx.doi.org/10.47111/jti.v12i2.529.

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The library is a building that contains a collection of books and other archivesin physical form neatly arranged in the building room, easy to find and use when neededby the reader.DDC (Decimal Dewey Classification) is the most widely used classificationsystem in the world. DDC is used to manage and provide access to a collection of booksin the library. The DDC is divided into ten main classes, which together cover the entireworld of knowledge. Consisting of ten main classes, one hundred divisions, and onethousand parts.Various problems found in the system are still manual, for example labeling andcategorizing books that are not suitable, therefore the authors chose this title to beappointed in the Practical Work College proposal because the author wanted to improvethe library work system, especially the Informatics Engineering Library of PalangkarayaUniversity. This application aims to improve the efficiency of library work. This libraryapplication was built using the PHP language (Hypertext Prepocessor). Dengandidukung oleh perangkat lunak seperti Net Beans, Photoshop dan XAMPP.
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Rotmianto, Mohamad, and Eko Wahyudi. "Developing Plugin e-DDC as an Additional Application for Senayan Library Management System with PHP Language Programming and MySQL Database." Record and Library Journal 2, no. 1 (January 5, 2018): 16. http://dx.doi.org/10.20473/rlj.v2-i1.2016.16-34.

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Between Senayan Library Management System or usually called SLiMS and e-DDC (electronic Dewey Decimal Classification) now is the most popular library application software which has a lot of user, because it is simple to use, has an updating guarantee from its developers and off course both of them are free of charge. Although SLiMS and e-DDC are different application at all, as practically they are recommended to be used togather for library management. SLiMS is used for library automation and e-DDC is to find collection’s classification. Many users of SLiMS and e-DDC ever give suggestions about developing SLiMS with e-DDC include in its database, and then librarians will be easier to manage their libraries. Because of that suggestion, finally a plugin as an additional application for SLiMS has been created and developed. That plugin was build with PHP language programming and MySQL database. The purpose of this paper is to enrich about reference of development of library application, especially those based on Free Open Source Software (FOSS). This paper use Research and Development Methods. And the result of this paper is Plugin e-DDC for SLiMS which has released on May, 2nd 2015, in order to celebrate “National Education Day”.
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Nurdiansyah, Y., A. Andrianto, and L. Kamshal. "New book classification based on Dewey Decimal Classification (DDC) law using tf-idf and cosine similarity method." Journal of Physics: Conference Series 1211 (April 2019): 012044. http://dx.doi.org/10.1088/1742-6596/1211/1/012044.

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Mugridge, Rebecca L. "Moving Beyond the Presentation Layer: Content and Context in the Dewey Decimal Classification (DDC) System." Library Collections, Acquisitions, & Technical Services 32, no. 3-4 (July 1, 2008): 161–62. http://dx.doi.org/10.1080/14649055.2008.10766220.

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Tarulli, Laurel. "Book Review: Moving Beyond the Presentation Layer: Content and Context in the Dewey Decimal Classification(DDC) System." Library Resources & Technical Services 52, no. 4 (October 1, 2008): 273–75. http://dx.doi.org/10.5860/lrts.52n4.273.

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Singh, K. P. "Expanded Schedule for Area Tables in Dewey Decimal Classification (DDC-23) for Rendering Indian States and Districts." Library Herald 52, no. 2 (2014): 182. http://dx.doi.org/10.5958/0976-2469.2014.00506.5.

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29

Costa, Deniz, and Marcos Luiz Cavalcanti de Miranda. "A Organização do Conhecimento sobre Umbanda e sua representação bibliográfica: uma análise exploratória a partir de registros bibliográficos." Informação & Informação 24, no. 3 (December 31, 2019): 154. http://dx.doi.org/10.5433/1981-8920.2019v24n3p154.

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Introdução: Apresenta o problema da representação do conhecimento sobre Umbanda. Contextualiza a relevância de se conhecer sua extensão para propor sistemas de organização do conhecimento que garantam sua representação e a visibilidade de sua produção. Pressupõe que as notações para a representação do conhecimento de e sobre Umbanda a partir da Dewey Decimal Classification (DDC) e dos Cabeçalhos de Assunto não são adequadas e perpetuam preconceitos e distorções. Objetivo: Conhecer de que maneira a Umbanda é representada nos registros bibliográficos do Catálogo da Fundação Biblioteca Nacional (FBN). Delineia-se em: (i) identificar os registros bibliográficos indexados sob o termo Umbanda; (ii) mapear a representação do conhecimento da Umbanda no Catálogo da FBN; e (iii) analisar essa representação pelas notações da DDC e Cabeçalhos de Assunto atribuídos. Metodologia: Pauta-se em uma busca combinada on-line no Catálogo da FBN para identificar manifestações cujos assuntos indexados versam sobre a Umbanda; coleta, trata por meio de softwares e analisa os dados obtidos; interpreta os resultados sob a ótica da Teoria do Conceito e das estruturas dos esquemas da DDC e Cabeçalhos de Assunto. Resultados: Mapeia-se a representação pela DDC e Cabeçalhos de Assunto que os registros bibliográficos sobre Umbanda na FBN apresentam, evidenciando os mais utilizados. Elabora críticas para evidenciar a problemática pressuposta. Conclusões: Evidencia que a representação do conhecimento reflete o entendimento da época na qual foi representado, refletindo na possibilidade da influência social, política e cultural encontrar-se manifestada. Identifica que a representação encontrada não reflete o entendimento atual e que é preciso refazer.
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., Prakash, and M. Krishnamurthy. "Knowledge and Skill Requirements for Health Science Library Professionals in India: A Survey." Asian Journal of Information Science and Technology 9, no. 1 (February 5, 2019): 8–13. http://dx.doi.org/10.51983/ajist-2019.9.1.2611.

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The survey method was considered most appropriate for this study because, it measures users background, experience and what they know about knowledge and skills in professional development in enhancing their knowledge in day to day working environment and it is well suited to the research questions taken up for this study. A total of 218 questionnaires were administered personally and 183 dully- filled-in questionnaire for received with response rate of 83.94% and were considered for analysis. The study findings were that 66.67% knew Dewey Decimal Classification (DDC), the National Library of Medicine Classification (NLM) with 14.75%.The response “No Answer” with 15.3% connotes these libraries have not classified their collection. Only 3.28% knew Library of Congress Classification though it is not much used in Indian libraries probably the respondents’ knowledge of this would be from their study. The mean and standard deviation are computed and ranks were assigned to each of the Medical databases corresponding to the respective designations. This comparison found MedInd/IndMed ranking is much higher than MEDLINE. So the data has no uniformity and conformity with the content and therefore the ranking are not commensurate with the subject content and wider use of the databases worldwide.
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M. Afdal, M. Afdal, and Muhammad Rosadi. "PENERAPAN ASSOCIATION RULE MINING UNTUK ANALISIS PENEMPATAN TATA LETAK BUKU DI PERPUSTAKAAN MENGGUNAKAN ALGORITMA APRIORI." Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi 5, no. 1 (February 28, 2019): 99. http://dx.doi.org/10.24014/rmsi.v5i1.7379.

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Perpustakaan adalah sebagai gedung yang memiliki koleksi bahan pustaka (buku, majalah danmedia lainnya) yang ditata atau diatur dengan cara tertentu agar mudah dimanfaatkan oleh pengguna secara cepat dan tepat. Perpustakaan Soeman Hs memiliki 60.583 judul dan 335.745 buku. Berdasarkan hasil wawancara dalam melakukan penempatan buku diperpustakaan, buku diletakan berdasarkan kategori buku yang telah diberikan penomoran yang disebut dewey decimal classification (DDC). Namun, dalam penempatan buku belum diatur dengan melihat tingkat keseringan pengunjung dalam meminjam buku tersebut. Selain itu, pengujung kesulitan dalam mencari kembali keberadaan buku yang sering dipinjam. Untuk mengatasi masalah tersebut, maka pada tugas akhir ini penulis akan mencari buku yang sering dipinjam secara bersamaan oleh pengunjung, dengan menganalisa data transaksi peminjamaan buku menggunakan metode association rule mining. Tugas akhir ini menggunakan Algoritma Apriori. Dari data 11.550 transaksi peminjaman buku selama 3 tahun yang telah diproses menghasilkan 4 rules dengan kombinasi item terbesar adalah kategori buku agama dan ilmu sosial sering dipinjam secara bersamaan dengan nilai support 11,71% dan confidence 41,43%. Selain itu, kategori buku teknologi dan ilmu sosial sering dipinjam secara bersamaan dengan nilai support 13,8% dan confidence 40,75%. Berdasarkan hasil tersebut disaran kepada pustakawan untuk menempatkan buku-buku tersebut pada rak yang berdekatan berdasarkan kategori buku.
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32

Yang, Ji-Ann. "Analysis of Concentrations of Loans by Using Book Circulation Data in Korea University Library." Publications 8, no. 4 (December 10, 2020): 53. http://dx.doi.org/10.3390/publications8040053.

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In this paper, data of almost 8 million loans of books recorded for 15 years by the Korea University Library are analyzed by using big data analytic techniques. During this period, book circulation decreased with an average annual rate of decline of 4.4%. The use factor of books in each Dewey decimal classification (DDC) class was evaluated to measure how efficiently books were used by library users. Loan frequencies of books were analyzed and meaningful results regarding loan concentrations and the half-lives of books were obtained. It was observed that 50% of the total loans in each year were for 20% of all borrowed books in that year. This phenomenon will be called the 20/50 loan rule, and the set of the top 20% most borrowed books, whose cumulative loan frequencies reach 50% of total loans, will be called a core collection. The 20/50 loan rule shows the loan concentration of library books. The extent of loan concentration gets stronger if loans for two or more consecutive years are concerned. It was found that with high probability, books in a core collection at a specific year are also categorized as a core collection in next years. Moreover, books categorized as a core collection in consecutive years have longer half-lives compared with all other circulating books.
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Sari, Dian Novita, and Desriyeni Desriyeni. "Klasifikasi Alat Musik Tradisional Minangkabau." Ilmu Informasi Perpustakaan dan Kearsipan 8, no. 1 (October 29, 2019): 88. http://dx.doi.org/10.24036/107301-0934.

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Abstract The writing of this paper aims to (1) find out the classification of Minangkabau traditional musical instruments; (2) knowing the process of classifying Minangkabau traditional musical instruments; (3) knowing the obstacles and efforts to overcome obstacles in the process of classifying Minangkabau traditional musical instruments. Writing this paper using descriptive research methods. Data was collected based on observations and interviews from various sources. Based on the results of the study it can be concluded as follows, first classifying the traditional Minangkabau musical instrument the first step taken is data collection and data compilation. Then classify musical instruments based on the types of musical instruments which are divided into five, namely striking, blowing, striking, picking and pressing musical instruments, but there are only four types of musical instruments in Minangkabau, namely striking, blowing, striking and picking instruments. The types of musical instruments are classified according to the guidelines on DDC (dewey decimal classification). The two processes of classifying traditional musical instruments have several parts, as follows: (1) Dewey's classification; (2) Determine the Main Class; (3) Determine Divisions; (4) Determine the Section. The three obstacles in the process of classifying traditional Minangkabau musical instruments are as follows: (1) lack of information regarding musical instruments in the Minangkabau Region including the area of origin of the musical instruments; (2) the difficulty of grouping musical instruments based on the type of musical instrument, because many names of musical instruments are almost the same. Efforts to overcome obstacles in the process of classifying Minangkabau traditional musical instruments are as follows: (1) conducting the process of collecting all data derived from several literatures and institutions that store traditional Minangkabau musical instruments; (2) pay close attention to musical instruments to be grouped according to the type of music.Keywords: classification; traditional musical instruments; minangkabau
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34

Satija, M. P. "DDC 21 Workbook:982Sydney W. Davis. DDC 21 Workbook: A Practical Introduction to the Dewey Decimal Classification (Occasional Monographs, 17). Wagga Wagga, NSW: Centre for Information Studies 1997. 58 pp, ISBN: 0949060399 (spiral bound)." Asian Libraries 7, no. 12 (December 1998): 436–37. http://dx.doi.org/10.1108/al.1998.7.12.436.2.

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35

Sullivan, Doreen. "DDC gem:The Theory and Practice of the Dewey Decimal Classification System.By M. P. Satija. Chandos Information Professionals Series. Oxford: Chandos Publishing, 2007. 206 pp. US$69.95 soft cover ISBN 9781843342342." Australian Library Journal 57, no. 4 (November 2008): 484–85. http://dx.doi.org/10.1080/00049670.2008.10722542.

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36

Comaromi, John P., and Mohinder Partap Satija. "Revising the Dewey Decimal Classification." KNOWLEDGE ORGANIZATION 15, no. 1 (1988): 17–20. http://dx.doi.org/10.5771/0943-7444-1988-1-17.

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37

Fountain, Joanna F. "Dewey Decimal Classification, 21st edition." Library Collections, Acquisitions, and Technical Services 25, no. 2 (June 2001): 236. http://dx.doi.org/10.1016/s1464-9055(00)00199-8.

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38

Fountain, Joanna F. "Dewey Decimal Classification, 21st edition." Library Collections, Acquisitions, & Technical Services 25, no. 2 (June 2001): 236. http://dx.doi.org/10.1080/14649055.2001.10765769.

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39

Taglienti, Paolina. "Dewey Decimal Classification, 22nd Edition." Library Collections, Acquisitions, & Technical Services 30, no. 3-4 (September 2006): 251. http://dx.doi.org/10.1080/14649055.2006.10766133.

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40

Kuhn, Tamara J. "Classifying Newspapers Using Dewey Decimal Classification." Library Resources & Technical Services 43, no. 2 (April 1, 1999): 106–13. http://dx.doi.org/10.5860/lrts.43n2.106.

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41

Yi, Hong, and Zhang Jin. "The Dewey Decimal Classification in China." KNOWLEDGE ORGANIZATION 23, no. 4 (1996): 213–15. http://dx.doi.org/10.5771/0943-7444-1996-4-213.

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42

Wursten, Richard B., Melvil Dewey, John P. Comaromi, Julianne Beall, Winton E. Matthews, Gregory R. New, Russell Sweeney, and John Clews. "Dewey Decimal Classification and Relative Index." Notes 48, no. 1 (September 1991): 143. http://dx.doi.org/10.2307/941824.

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43

Fountain, Joanna F. "Learn Dewey Decimal Classification (edition 21)." Library Collections, Acquisitions, and Technical Services 25, no. 2 (June 2001): 240–41. http://dx.doi.org/10.1016/s1464-9055(00)00206-2.

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44

Taylor, Arlene G. "Teaching the Dewey Decimal Classification System." Cataloging & Classification Quarterly 42, no. 3-4 (August 2006): 97–117. http://dx.doi.org/10.1300/j104v42n03_03.

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45

Fountain, Joanna F. "Learn Dewey Decimal Classification (edition 21)." Library Collections, Acquisitions, & Technical Services 25, no. 2 (June 2001): 240–41. http://dx.doi.org/10.1080/14649055.2001.10765774.

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46

McAllister-Harper, Desretta. "Dewey Decimal Classification in the Online' Environment:." Cataloging & Classification Quarterly 11, no. 1 (June 7, 1990): 45–58. http://dx.doi.org/10.1300/j104v11n01_05.

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47

Mitchell, Joan S. "Options in the Dewey Decimal Classification System:." Cataloging & Classification Quarterly 19, no. 3-4 (July 6, 1995): 89–103. http://dx.doi.org/10.1300/j104v19n03_08.

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48

Pacey, Philip. "The Classification of Literature in the Dewey Decimal Classification." Cataloging & Classification Quarterly 9, no. 4 (May 16, 1989): 101–7. http://dx.doi.org/10.1300/j104v09n04_08.

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49

Lessard, Sophie. "Pour la documentation en entomologie : LCC ou DDC ?" Documentation et bibliothèques 39, no. 1 (February 13, 2015): 7–10. http://dx.doi.org/10.7202/1028595ar.

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Les insectes, largement étudiés dans la documentation, occupent une place importante au sein des systèmes de classification de Dewey et de la Library of Congress. Bien que la classification de la Library of Congress (LCC) soit davantage développée en ce qui a trait à la taxinomie entomologique, l’arrangement de la classification Dewey (DDC) s’avère plus logique et plus conforme à la classification évolutive. DDC permet également un plus grand nombre de possibilités en ce qui concerne la distribution géographique des insectes et les différents aspects sous lesquels ils sont abordés.
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Wang, Jun. "An extensive study on automated Dewey Decimal Classification." Journal of the American Society for Information Science and Technology 60, no. 11 (November 2009): 2269–86. http://dx.doi.org/10.1002/asi.21147.

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