From raw affiliations to organization identifiers

TL;DR

This paper introduces AffRo, a novel method for accurate affiliation matching in scholarly metadata, addressing complex affiliation strings with advanced parsing and disambiguation, supported by a new curated dataset for benchmarking.

Contribution

The paper presents AffRo, a new approach for affiliation matching that handles complex strings, and introduces AffRoDB, a curated dataset for systematic evaluation.

Findings

AffRo outperforms existing methods in accuracy.

AffRoDB enables robust benchmarking of affiliation algorithms.

The approach effectively disambiguates multiple organizations in affiliation strings.

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.