Author name disambiguation forPubMed

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2021

Author Name Disambiguation (AND) is the task of resolving which author mentions in a bibliographic database refer to the same real-world person, and is a critical ingredient of digital library applications such as search and citation analysis. While many AND algorithms have been proposed, comparing them is difficult because they often employ distinct features and are evaluated on different datasets. In response to this challenge, we present S2AND, a unified benchmark dataset for AND on scholarly papers, as well as an open-source reference model implementation. Our dataset harmonizes eight disparate AND datasets into a uniform format, with a single rich feature set drawn from the Semantic Scholar (S2) database. Our evaluation suite for S2AND reports performance split by facets like publication year and number of papers, allowing researchers to track both global performance and measures of fairness across facet values. Our experiments show that because previous datasets tend to cover idiosyncratic and biased slices of the literature, algorithms trained to perform well on one on them may generalize poorly to others. By contrast, we show how training on a union of datasets in S2AND results in more robust models that perform well even on datasets unseen in training. The resulting AND model also substantially improves over the production algorithm in S2, reducing error by over 50% in terms of B 3 F1. We release our unified dataset, model code, trained models, and evaluation suite to the research community. 1 Index Terms-Digital libraries, Author name disambiguation, Out-of-domain evaluation.

AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

As an information retrieval system, PubMed(®) aims at providing efficient access to documents cited in MEDLINE(®). For this purpose, it relies on matching representations of documents, as provided by authors and indexers to user queries. In this paper, we describe the growth of author keywords in biomedical journal articles and present a comparative study of author keywords and MeSH(®) indexing terms assigned by MEDLINE indexers to PubMed Central Open Access articles. A similarity metric is used to assess automatically the relatedness between pairs of author keywords and indexing terms. A set of 300 pairs is manually reviewed to evaluate the metric and characterize the relationships between author keywords and indexing terms. Results show that author keywords are increasingly available in biomedical articles and that over 60% of author keywords can be linked to a closely related indexing term. Finally, we discuss the potential impact of this work on indexing and terminology developm...

Synthesis lectures on information concepts, retrieval, and services, 2020

This book deals with a hard problem that is inherent to human language: ambiguity. In particular, we focus on author name ambiguity, a type of ambiguity that exists in digital bibliographic repositories, which occurs when an author publishes works under distinct names or distinct authors publish works under similar names. This problem may be caused by a number of reasons, including the lack of standards and common practices, and the decentralized generation of bibliographic content. As a consequence, the quality of the main services of digital bibliographic repositories such as search, browsing, and recommendation may be severely affected by author name ambiguity. The focal point of the book is on automatic methods, since manual solutions do not scale to the size of the current repositories or the speed in which they are updated. Accordingly, we provide an ample view on the problem of automatic disambiguation of author names, summarizing the results of more than a decade of research on this topic conducted by our group, which were reported in more than a dozen publications that received over 900 citations so far, according to Google Scholar. We start by discussing its motivational issues (Chapter 1). Next, we formally define the author name disambiguation task (Chapter 2) and use this formalization to provide a brief, taxonomically organized, overview of the literature on the topic (Chapter 3). We then organize, summarize and integrate the efforts of our own group on developing solutions for the problem that have historically produced state-of-the-art (by the time of their proposals) results in terms of the quality of the disambiguation results. Thus, Chapter 4 covers HHC -Heuristic-based Clustering, an author name disambiguation method that is based on two specific real-world assumptions regarding scientific authorship. Then, Chapter 5 describes SAND -Self-training Author Name Disambiguator and Chapter 6 presents two incremental author name disambiguation methods, namely INDi -Incremental Unsupervised Name Disambiguation and INC-Incremental Nearest Cluster. Finally, Chapter 7 provides an overview of recent author name disambiguation methods that address new specific approaches such as graph-based representations, alternative predefined similarity functions, visualization facilities and approaches based on artificial neural networks. The chapters are followed by three appendices that cover, respectively: (i) a pattern matching function for comparing proper names and used by some of the methods addressed in this book; (ii) a tool for generating synthetic collections of citation records for distinct experimental tasks; and (iii) a number of datasets commonly used to evaluate author name disambiguation methods. In summary, the book organizes a large body of knowledge and work in the area of author name disambiguation in the last decade, hoping to consolidate a solid basis for future developments in the field.

2020

Entity resolution is a challenging and hot research area in the field of Information Systems since last decade. Author Name Disambiguation (AND) in Bibliographic Databases (BD) like DBLP , Citeseer , and Scopus is a specialized field of entity resolution. Given many citations of underlying authors, the AND task is to find which citations belong to the same author. In this survey, we start with three basic AND problems, followed by need for solution and challenges. A generic, five-step framework is provided for handling AND issues. These steps are; (1) Preparation of dataset (2) Selection of publication attributes (3) Selection of similarity metrics (4) Selection of models and (5) Clustering Performance evaluation. Categorization and elaboration of similarity metrics and methods are also provided. Finally, future directions and recommendations are given for this dynamic area of research.

2012

Author name ambiguity in the context of bibliographic citations is a very hard problem. It occurs when there are citation records of a same author under distinct names or when there exists citation records belonging to distinct authors with very similar names. Among the several methods proposed in the literature, the most effective ones are those that perform a direct assignment of the records to their respective authors by means of the application of supervised machine learning techniques. However, those methods usually need large amounts of labeled training examples to properly disambiguate the author names. To deal with this issue, in previous work, we have proposed a method that automatically obtains and labels the training examples, showing competitive performance compared to representative author name disambiguation methods. In this work, we propose to improve our previous method by exploiting user relevance feedback. In more details we select a very small portion of the citation records for which our method was mostly unsure about the correct authorship and ask the administrators for labeling them. This feedback is then used to improve the effectiveness of the whole process. In our experimental evaluation, we observed that with a very small labeling effort (usually around 5% of the records), the overall disambiguation effectiveness improves by almost 10% on average, with gains of up to 61% in some of the largest ambiguous groups.

Journal of Open Source Software

International Journal of Software Engineering and Its Applications, 2016

When using search engine services to search for scholarly articles, obtaining quick and accurate search results from a huge set of scholarly information is always important. However, most of the domestic and foreign search engine services for scholarly articles present a broad range of the results that correspond to the query of the researcher's name. Such results contribute in lowering the search precision and require users to spend time and effort to verify the results and find the necessary information. Such a problem is called "author ambiguity", while solving this problem is called "author disambiguation." An author disambiguation method classifies the authors with the same name into an actual person. By resolving author ambiguity, better search results can be obtained; this increases the recall rate and accuracy when searching for scholarly articles. In order to resolve author ambiguity in this paper, we shall expand the co-author network and identify the author using the co-author network information and basic bibliographic information as the features for machine learning Support Vector Machine. To examine the effectiveness of the proposed method, we test the author disambiguation method by targeting 92,100 IT-related scholarly data generated in Korea. Author disambiguation results through the expansion of co-author network are shown to have an F-1 measure of 94.79%. The result confirms that the author disambiguation method through the implementation of the co-author network is effective.

Proceedings of the 2nd …, 2007

In this paper, we propose a new method of citation data clustering for author name disambiguation. Most citation data appearing in the reference section of scientific papers include the coauthor first names with their initials. Hence, we often search citation data by using such an abbreviated name, e.g. "S. Lee" or "J. Chen", and consequently obtain many irrelevant data in the search result, because such an abbreviated name refers to many different persons. In this paper, we propose a method of citation data clustering to construct clusters each of which includes only citation data corresponding to a unique author. Our clustering method is based on a probabilistic model which is an extension of the naive Bayes mixture model. Since our model has two hidden variables, we call it two-variable mixture model. In the evaluation experiment, we used the well-known DBLP data set. The results show that the two-variable mixture model can achieve a better balance between precision and recall than the naive Bayes mixture model.

Journal of Computing and Security, 2015

Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries - JCDL '13, 2013

This paper presents a new name disambiguation method that exploits user feedback on ambiguous references across iterations. An unsupervised step is used to define pure training samples, and a hybrid supervised step is employed to learn a classification model for assigning references to authors. Our classification scheme combines the Optimum-Path Forest (OPF) classifier with complex reference similarity functions generated by a Genetic Programming framework. Experiments demonstrate that the proposed method yields better results than state-of-the-art disambiguation methods on two traditional datasets.