A Novel Approach for Author Name Disambiguation Using Ranking Confidence

In digital libraries, ambiguous author names may occur because of the existence of multiple authors with the same name or different name variations for the same person. In recent years, name disambiguation has become a major challenge when integrating data from multiple sources in bibliographic digital libraries. Most of the previous works solve this issue by using many attributes, such as coauthors, title of articles/publications, topics of articles, and years of publications. However, in most cases, we can only get the coauthor and title attributes. In this paper, we propose an approach which is based on Hierarchical Agglomerative Clustering (HAC) and only use the coauthor and title attributes, but can more effectively identify the disambiguation authors. The whole algorithm can divide into two stages. In the first stage, we employ a pair-wise grouping algorithm which is based on coauthors’name to group records into clusters. Then, we merge two clusters if the similarity of the article titles from two clusters reach the threshold. Here, we use three kinds of similarity algorithms such as Jaccard Similarity, Cosine Similarity and Euclidean Distance to compare the similarity between the titles of two clusters. To minimize the risk of using only one similarity metric, we design the concept of ranking confidence to measure the confidence of different similarity meausrements. The ranking confidence decides which similarity measure to use when merging clusters. In the experiments, we use PairPresicion, PairRecall and PairF1 score to evaluate our method and compare with other methods. Experimental results indicate that our method significantly outperforms the baseline methods: HAC, K-means and SACluster when only use coauthor and title attributes.