Implementation of a Classification Server to Support Metadata Organization for Long Term Preservation Systems

Kopácsi, Sándor; Hudak, Rastislav; Ganguly, Raman

doi:10.31263/voebm.v70i2.1897

Cited by 2 publications

References 3 publications

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Automatic Classifier of Scientific Contents

Machado

Sá

2021

Advances in Intelligent Systems and Computing

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The growth of scientific production, associated with the increase in the complexity of scientific contents, makes the classification of these contents highly subjective and subject to misinterpretation. The taxonomy on which this classification process is based does not follow the scientific areas' changes. These classification processes are manually carried out and are therefore subject to misclassification. A classification process that allows automation and implements intelligent algorithms based on Machine Learning algorithms presents a possible solution to subjectivity in classification. Although it does not solve the inadequacy of taxonomy, this work shows this possibility by developing a solution to this problem. In conclusion, this work proposes a solution to classify scientific content based on the title, abstract, and keywords through Natural Language Processing techniques and Machine Learning algorithms to organize scientific content in scientific domains.

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Automatic Classifier of Scientific Contents

Machado

Sá

2021

Advances in Intelligent Systems and Computing

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Aspects of the Long-Term Preservation of Digitized Catalogue Data: Analysis of the Databases of Integrated Collection Management Systems

Simon

Kiszl

2021

Preservation, Digital Technology &Amp; Culture

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During this research, the catalogues of more than 200 libraries and museums of Hungary and its neighboring countries were examined. The authors calculated the amount and the size of the metadata and of the full content records in the databases of their collection management systems, as well as the size and the type of the full content data and the size of the databases. By analyzing the results, the goal was to answer the following three questions: (1) Can any significant difference be established between the results according to country, nationality, or type of institution?; (2) How large is a metadata record or a full content record?; (3) Is it possible to establish a methodology for selecting a representative sample of institutions to facilitate further research? For planning the costs of data management, the size of the databases, the number of metadata records, and the variability of metadata and media records shall all be considered. A distinction should be made between the indispensable “primary” data to be preserved for a long time, and the “secondary” data units which are derived from the primary data. It is investigated in this article how to establish the size of primary data in the databases of collection management systems.

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Implementation of a Classification Server to Support Metadata Organization for Long Term Preservation Systems

Cited by 2 publications

References 3 publications

Automatic Classifier of Scientific Contents

Automatic Classifier of Scientific Contents

Aspects of the Long-Term Preservation of Digitized Catalogue Data: Analysis of the Databases of Integrated Collection Management Systems

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