Automatic General Metadata Extraction and Mapping in an HDF5 Use-case

Benedikt Heinrichs, Nils Preuß, Marius Politze, Matthias S. Müller, Peter F. Pelz

2021

Abstract

Extracting interoperable metadata from data entities is not an easy task. A method for this would need to extract non-interoperable metadata values first and then map the extracted metadata to some sensible representation. In the case of HDF5 files, metadata annotation is already an option, making it an easy target for extracting these non-interoperable metadata values. This paper describes a use-case, that utilizes this property to automatically annotate their data. However, the issue arises, that these metadata values are not reusable, due to their missing interoperability, and validatable since they do not follow any defined metadata schema. Therefore, this paper provides a solution for mapping the defined metadata values to interoperable metadata by extracting them first using a general metadata extraction pipeline and then proposing a method for mapping them. This method can receive a number of application profiles and creates interoperable metadata based on the best fit. The method is validated against the introduced use-case and shows promising results for other research domains as well.

Paper Citation

in Harvard Style

Heinrichs B., Preuß N., Politze M., Müller M. and Pelz P. (2021). Automatic General Metadata Extraction and Mapping in an HDF5 Use-case. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR; ISBN 978-989-758-533-3, SciTePress, pages 172-179. DOI: 10.5220/0010654100003064

in Bibtex Style

@conference{kdir21,
author={Benedikt Heinrichs and Nils Preuß and Marius Politze and Matthias S. Müller and Peter F. Pelz},
title={Automatic General Metadata Extraction and Mapping in an HDF5 Use-case},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR},
year={2021},
pages={172-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010654100003064},
isbn={978-989-758-533-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR
TI - Automatic General Metadata Extraction and Mapping in an HDF5 Use-case
SN - 978-989-758-533-3
AU - Heinrichs B.
AU - Preuß N.
AU - Politze M.
AU - Müller M.
AU - Pelz P.
PY - 2021
SP - 172
EP - 179
DO - 10.5220/0010654100003064
PB - SciTePress