Document - From raw affiliations to organization identifiers

2026

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From raw affiliations to organization identifiers

Kallipoliti M., Chatzopoulos S., Baglioni M., Adamidi E., Koloveas P., Vergoulis T.

Affiliation matching Persistent identifiers

Accurate affiliation matching, which links affiliation strings to standardized organization identifiers, is critical for improving research metadata quality, facilitating comprehensive bibliometric analyses, and supporting data interoperability across scholarly knowledge bases. Existing approaches fail to handle the complexity of affiliation strings that often include mentions of multiple organizations or extraneous information. In this paper, we present AffRo, a novel approach designed to address these challenges, leveraging advanced parsing and disambiguation techniques. We also introduce AffRoDB, an expert-curated dataset to systematically evaluate affiliation matching algorithms, ensuring robust benchmarking. Results demonstrate the effectiveness of AffRo in accurately identifying organizations from complex affiliation strings.

Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16097, pp. 111-126. fin, 2025

Publisher: Springer

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@inproceedings{oai:iris.cnr.it:20.500.14243/556082,
	title = {From raw affiliations to organization identifiers},
	author = {Kallipoliti M. and Chatzopoulos S. and Baglioni M. and Adamidi E. and Koloveas P. and Vergoulis T.},
	publisher = {Springer},
	doi = {10.1007/978-3-032-05409-8_8},
	booktitle = {LECTURE NOTES IN COMPUTER SCIENCE, vol. 16097, pp. 111-126. fin, 2025},
	year = {2026}
}