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Author name disambiguation for P ub M ed
Author(s) -
Liu Wanli,
Islamaj Doğan Rezarta,
Kim Sun,
Comeau Donald C.,
Kim Won,
Yeganova Lana,
Lu Zhiyong,
Wilbur W. John
Publication year - 2014
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23063
Subject(s) - computer science , cluster analysis , pairwise comparison , ambiguity , transitive relation , artificial intelligence , similarity (geometry) , word error rate , information retrieval , hierarchical clustering , natural language processing , machine learning , data mining , mathematics , image (mathematics) , combinatorics , programming language
Log analysis shows that P ub M ed users frequently use author names in queries for retrieving scientific literature. However, author name ambiguity may lead to irrelevant retrieval results. To improve the P ub M ed user experience with author name queries, we designed an author name disambiguation system consisting of similarity estimation and agglomerative clustering. A machine‐learning method was employed to score the features for disambiguating a pair of papers with ambiguous names. These features enable the computation of pairwise similarity scores to estimate the probability of a pair of papers belonging to the same author, which drives an agglomerative clustering algorithm regulated by 2 factors: name compatibility and probability level. With transitivity violation correction, high precision author clustering is achieved by focusing on minimizing false‐positive pairing. Disambiguation performance is evaluated with manual verification of random samples of pairs from clustering results. When compared with a state‐of‐the‐art system, our evaluation shows that among all the pairs the lumping error rate drops from 10.1% to 2.2% for our system, while the splitting error rises from 1.8% to 7.7%. This results in an overall error rate of 9.9%, compared with 11.9% for the state‐of‐the‐art method. Other evaluations based on gold standard data also show the increase in accuracy of our clustering. We attribute the performance improvement to the machine‐learning method driven by a large‐scale training set and the clustering algorithm regulated by a name compatibility scheme preferring precision. With integration of the author name disambiguation system into the P ub M ed search engine, the overall click‐through‐rate of P ub M ed users on author name query results improved from 34.9% to 36.9%.