Beyond Species: Enhancing Botanical Data Integrity Using Similarity Metrics in Authorship Attribution
DOI:
https://doi.org/10.5753/jidm.2026.5847Keywords:
Botanical Databases, Text Similarity, International Code of NomenclatureAbstract
This extended study builds upon prior work exploring the deduplication of botanical authorship records governed by the International Code of Nomenclature (ICN). We introduce new datasets (Sargassaceae and Agaricaceae) to evaluate the performance of multiple text similarity algorithms, including Jaccard, Levenshtein, Jaro-Winkler, Metaphone, N-grams, Smith-Waterman, and Fingerprinting. Our updated methodology incorporates enhanced preprocessing strategies, new threshold calibration techniques, and comprehensive metric-based evaluations (precision, recall, and F1 score). The results reaffirm the robustness of Smith-Waterman and highlight the dataset-dependent behavior of Metaphone and Fingerprinting. This expanded analysis contributes to a more generalized understanding of text similarity challenges in biological databases and reinforces the importance of tailored algorithm selection based on taxonomic structure and data quality.
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