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Towards an extensible measurement of metadata quality

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Published:01 June 2017Publication History

ABSTRACT

This paper describes the structure of an extensible metadata quality assessment framework, which supports multiple metadata schemas, and is flexible enough to work with new schemas. The software has to be scalable to be able to process huge amount of metadata records within a reasonable time. Fundamental requirements that need to be considered during the design of such a software are i) the abstraction of the metadata schema (in the context of the measurement process), ii) how to address distinct parts within metadata records, iii) the workflow of the measurement, iv) a common and powerful interface for the individual metrics, and v) interoperability with Java and REST APIs.

References

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  4. Juliane Stiller and Péter Király. 2017. Multilinguality of Metadata. Measuring the Multilingual Degree of Europeana's Metadata. In Everything Changes, Everything Stays the Same? Understanding Information Spaces. Proceedings of the 15th International Symposium of Information Science (ISI 2017) (Schriften zur Informationswissenschaft), M. Gäde, V. Trkulja, and V. Petras (Eds.). Verlag Werner Hülsbusch, 164--176. https://www.researchgate.net/publication/314879735_Multilinguality_of_Metadata_Measuring_the_Multilingual_Degree_of_Europeana%27s_MetadataGoogle ScholarGoogle Scholar
  5. Osma Suominen and Eero Hyvönen. 2012. Improving the Quality of SKOS Vocabularies with Skosify. In Knowledge Engineering and Knowledge Management: 18th International Conference, EKAW 2012, Galway City, Ireland, October 8-12, 2012. (Lecture Notes in Computer Science), Annette ten Teije (Ed.), Vol. 7603. Springer, 383--397. https://doi.org/10.1007/978-3-642-33876-2_34Google ScholarGoogle Scholar

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  1. Towards an extensible measurement of metadata quality

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            DATeCH2017: Proceedings of the 2nd International Conference on Digital Access to Textual Cultural Heritage
            June 2017
            179 pages
            ISBN:9781450352659
            DOI:10.1145/3078081

            Copyright © 2017 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 1 June 2017

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            DATeCH2017 Paper Acceptance Rate29of37submissions,78%Overall Acceptance Rate60of86submissions,70%

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