PyResolveMetrics: A Standards-Compliant and Efficient Approach to Entity Resolution Metrics

Andrei Olar, Laura Dioşan

2024

Abstract

Entity resolution, the process of discerning whether multiple data refer to the same real-world entity, is crucial across various domains, including education. Its quality assessment is vital due to the extensive practical applications in fields such as analytics, personalized learning or academic integrity. With Python emerging as the predominant programming language in these areas, this paper attempts to fill in a gap when evaluating the qualitative performance of entity resolution tasks by proposing a novel consistent library dedicated exclusively for this purpose. This library not only facilitates precise evaluation but also aligns with contemporary research and application trends, making it a significant tool for practitioners and researchers in the field.

Paper Citation

in Harvard Style

Olar A. and Dioşan L. (2024). PyResolveMetrics: A Standards-Compliant and Efficient Approach to Entity Resolution Metrics. In Proceedings of the 16th International Conference on Computer Supported Education - Volume 1: CSEDU; ISBN 978-989-758-697-2, SciTePress, pages 257-263. DOI: 10.5220/0012546300003693

in Bibtex Style

@conference{csedu24,
author={Andrei Olar and Laura Dioşan},
title={PyResolveMetrics: A Standards-Compliant and Efficient Approach to Entity Resolution Metrics},
booktitle={Proceedings of the 16th International Conference on Computer Supported Education - Volume 1: CSEDU},
year={2024},
pages={257-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012546300003693},
isbn={978-989-758-697-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Computer Supported Education - Volume 1: CSEDU
TI - PyResolveMetrics: A Standards-Compliant and Efficient Approach to Entity Resolution Metrics
SN - 978-989-758-697-2
AU - Olar A.
AU - Dioşan L.
PY - 2024
SP - 257
EP - 263
DO - 10.5220/0012546300003693
PB - SciTePress