A fast and integrative algorithm for clustering performance evaluation in author name disambiguation

Bridging the gap in author names: building an enhanced author name dataset for biomedical literature system

OBJECTIVE

Author name incompleteness, referring to only first initial available instead of full first name, is a long-standing problem in MEDLINE and has a negative impact on biomedical literature systems. The purpose of this study is to create an Enhanced Author Names (EAN) dataset for MEDLINE that maximizes the number of complete author names.

MATERIALS AND METHODS

The EAN dataset is built based on a large-scale name comparison and restoration with author names collected from multiple literature databases such as MEDLINE, Microsoft Academic Graph, and Semantic Scholar. We assess the impact of EAN on biomedical literature systems by conducting comparative and statistical analyses between EAN and MEDLINE's author names dataset (MAN) on 2 important tasks, author name search and author name disambiguation.

RESULTS

Evaluation results show that EAN improves the number of full author names in MEDLINE from 69.73 million to 110.9 million. EAN not only restores a substantial number of abbreviated names prior to the year 2002 when the NLM changed its author name indexing policy but also improves the availability of full author names in articles published afterward. The evaluation of the author name search and author name disambiguation tasks reveal that EAN is able to significantly enhance both tasks compared to MAN.

CONCLUSION

The extensive coverage of full names in EAN suggests that the name incompleteness issue can be largely mitigated. This has significant implications for the development of an improved biomedical literature system. EAN is available at https://zenodo.org/record/10251358, and an updated version is available at https://zenodo.org/records/10663234.

ORCID-linked labeled data for evaluating author name disambiguation at scale

How can we evaluate the performance of a disambiguation method implemented on big bibliographic data? This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale. This study demonstrates the potential by evaluating the disambiguation performances of Author-ity2009 (which algorithmically disambiguates author names in MEDLINE) using 3 million name instances that are automatically labeled through linkage to 5 million ORCID researcher profiles. Results show that although ORCID-linked labeled data do not effectively represent the population of name instances in Author-ity2009, they do effectively capture the ‘high precision over high recall’ performances of Author-ity2009. In addition, ORCID-linked labeled data can provide nuanced details about the Author-ity2009’s performance when name instances are evaluated within and across ethnicity categories. As ORCID continues to be expanded to include more researchers, labeled data via ORCID-linkage can be improved in representing the population of a whole disambiguated data and updated on a regular basis. This can benefit author name disambiguation researchers and practitioners who need large-scale labeled data but lack resources for manual labeling or access to other authority sources for linkage-based labeling. The ORCID-linked labeled data for Author-ity2009 are publicly available for validation and reuse.