From raw affiliations to organization identifiers

• URL: http://arxiv.org/abs/2505.07577v2
• Date: Tue, 13 May 2025 09:27:19 GMT
• Title: From raw affiliations to organization identifiers
• Authors: Myrto Kallipoliti, Serafeim Chatzopoulos, Miriam Baglioni, Eleni Adamidi, Paris Koloveas, Thanasis Vergoulis,
• Abstract summary: Existing approaches fail to handle the complexity of affiliation strings that often include mentions of multiple organizations or extraneous information.<n>We present AffRo, a novel approach designed to address these challenges, leveraging advanced parsing and disambiguation techniques.<n>Results demonstrate the effectiveness of AffRp in accurately identifying organizations from complex affiliation strings.
• Score: 0.343054185715673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 AffRp in accurately identifying organizations from complex affiliation strings.

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