Effective Unsupervised Author Disambiguation with Relative Frequencies

Abstract

This work addresses the problem of author name homonymy in the Web of Science. Aiming for an efficient, simple and straightforward solution, we introduce a novel probabilistic similarity measure for author name disambiguation based on feature overlap. Using the researcher-ID available for a subset of the Web of Science, we evaluate the application of this measure in the context of agglomeratively clustering author mentions. We focus on a concise evaluation that shows clearly for which problem setups and at which time during the clustering process our approach works best. In contrast to most other works in this field, we are skeptical towards the performance of author name disambiguation methods in general and compare our approach to the trivial single-cluster baseline. Our results are presented separately for each correct clustering size as we can explain that, when treating all cases together, the trivial baseline and more sophisticated approaches are hardly distinguishable in terms of evaluation results. Our model shows state-of-the-art performance for all correct clustering sizes without any discriminative training and with tuning only one convergence parameter.

Model Reuse in Machine Learning for Author Name Disambiguation: An Exploration of Transfer Learning

“…Considering that previous studies have shown that author name and coauthorship play the most important roles in distinguishing authors [5,18,19,47], disambiguation models learned on AMINER may have incorrectly assigned author name instances with similar author name strings and coauthor names to belong to different people, producing a greater number of false negatives and leading to the lower observed recall scores for transfer learning from AMINER descried in our discussion of Fig. 4~7 above.…”

Author name disambiguation of bibliometric data: A comparison of several unsupervised approaches

“…This allows including attributes flexibly without the necessity to specify the corresponding weights separately. The results reported in Backes (2018a) suggest that using equal weights for all attributes produces good results. Each iteration of the clustering process merges all pairs of current clusters whose similarity exceeds l. The quality limit l is designed to have a linear dependence on the block size |X|, whereby the parameter specifies this relationship (see Eq.…”

“…This threshold can be determined globally for all pairs of author mentions being compared, or it can vary depending on the number of author mentions within a block that refer to a single name representation. Block-size-dependent thresholds try to reduce the problem of an increasing number of false links for a higher number of comparisons between author mentions; that is, for larger name blocks (Backes, 2018a;Caron & van Eck, 2014).…”

“…Higher thresholds are therefore used for larger blocks to reduce the risk of incorrectly linked author mentions. Backes (2018a) proposed an approach that starts by considering each author mention as one cluster. An agglomerative clustering algorithm is then employed that iteratively merges clusters (starting with single author mentions as clusters, then merging clusters of several author mentions) if they are sufficiently similar; that is, two clusters are connected if their similarity exceeds a quality limit l. The similarity metric indicating how similar two clusters are takes into account the specificity of the author mentions' metadata.…”

“…First, we calculate pairwise precision (P pair ), pairwise recall (R pair ), and pairwise F1 (F1 pair ) for each approach. These metrics have been used in many studies (e.g., Backes, 2018a;Caron & van Eck, 2014;Levin et al, 2012). Whereas pairwise precision measures how many links between author mentions in detected clusters are correct, pairwise recall measures how many links between author mentions of real-world authors are correctly detected.…”