An unsupervised heuristic‐based hierarchical method for name disambiguation in bibliographic citations

Abstract

Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement. In this article, we present a heuristic-based hierarchical clustering method to deal with this problem. The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (e.g., coauthor names, work title, and publication venue title). During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion. In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature. We present comparisons of results using each considered attribute separately (i.e., coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together. These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours. Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters.

Evaluating entity resolution results

“…• K [8]: Combines the average similarity of clusters in R with respect to those in S and the average similarity of clusters in S with respect to those in R.…”

“…The K measure [8,2] sums the similarities of all cluster pairs and is defined as the geometric mean of the Average Cluster Purity (ACP) and the Average Author Purity (AAP). (Here, Author can be thought of as a cluster in the gold standard.)…”

Detecting referential inconsistencies in electronic CV datasets

“…They use a mix of techniques. While some use similarity functions [2,7,12,18,21,27,30], others use learning techniques [1,14,16,28,32,35], heuristics [17,19,20,24], classifiers [9,10,34] and clustering methods [11,31].…”

Author Name Disambiguation Using a New Categorical Distribution Similarity

“…The reasons for developing the two-stage clustering are twofold: First, coauthors generally provide stronger evidence than other features, based on which the generated cluster usually comprises of papers of the same author, but the papers of an author may distribute among multiple clusters ( [3]); Second, the venue and title features are relatively weak evidence, based on which we can further merge clusters from the same author.…”

“…For each paper, we consider 3 features: coauthors, published venue and title, by following the setting used in previous work [5,3,12]. Under this setting, our proposed method can be general and applicable to the existing bibliography databases, e.g., DBLP, since they contain information on the three features for each paper.…”