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Cheng, Y.-Y.; Xia, Y.: ¬A systematic review of methods for aligning, mapping, merging taxonomies in information sciences (2023) 0.21
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Abstract

The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics. Design/methodology/approach The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on "taxonomies." Findings They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions. Research limitations/implications The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research. Originality/value There is no existing comprehensive review on the alignment of "taxonomies". Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.

Content

Vgl.: DOI: 10.1108/JD-01-2023-0003.

Source

Journal of documentation. 79(2023) no.?, S.xxx-xxx

Year

2023

Candela, G.: ¬An automatic data quality approach to assess semantic data from cultural heritage institutions (2023) 0.09

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Date: 22. 6.2023 18:23:31
Source: Journal of the Association for Information Science and Technology. 74(2023) no.7, S.866-878
Year: 2023

Gabler, S.: Thesauri - a Toolbox for Information Retrieval (2023) 0.05

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Content: Vgl.: DOI: 10.1515/bfp-2023-0003.
Footnote: https://doi.org/10.1515/bfp-2023-0003.
Source: Bibliothek: Forschung und Praxis. 47(2023) H.2, S.189-199
Year: 2023

Gabler, S.: Vergabe von DDC-Sachgruppen mittels eines Schlagwort-Thesaurus (2021) 0.03
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Content

Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.

Ahmed, M.; Mukhopadhyay, M.; Mukhopadhyay, P.: Automated knowledge organization : AI ML based subject indexing system for libraries (2023) 0.02

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Source: DESIDOC journal of library and information technology. 43(2023) no.1, S.45-54
Year: 2023

Rölke, H.; Weichselbraun, A.: Ontologien und Linked Open Data (2023) 0.02

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Year: 2023

Marcondes, C.H.: Towards a vocabulary to implement culturally relevant relationships between digital collections in heritage institutions (2020) 0.01

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Date: 4. 3.2020 14:22:41

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