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Using Elasticsearch for entity recognition in affiliation disambiguation

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arxiv 2110.01958 v1 pith:NWD2BPCW submitted 2021-10-05 cs.DL

Anne L'Hôte , Eric Jeangirard This is my paper · ORCID
classification cs.DL
keywords alignmentautomaticmethodelasticsearchgithubproposedrecognitionregistries
verification ladder T0 review T1 audit T2 compute T3 formal
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Automatic recognition of affiliations in the metadata of scholarly publications is a key point for monitoring and analyzing trends in scientific production, especially in an open science context. We propose an automatic alignment method on registries, based on Elasticsearch. The proposed method is modular and leaves the choice of the alignment criteria to the user, allowing him to keep control over the precision and recall of the method. An implementation is proposed for an automatic alignment on three registries: countries, GRID.ac and RNSR (research laboratory directory in France) on the Github https://github.com/dataesr/matcher and the performances are analyzed in this paper.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Works-magnet: Accelerating Metadata Curation for Open Science

    cs.DL 2025-06 conditional novelty 5.0 of 10

    Works-magnet is an open-source, public-facing system for correcting affiliation metadata in OpenAlex, with more than 71,000 correction requests logged so far.

  2. Mapping scientific communities at scale

    cs.DL 2025-01 conditional novelty 3.0 of 10

    An open-source pipeline maps millions of French publications into interactive community networks by pre-filtering the strongest co-occurrence links and labeling clusters with an LLM.

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