Author name ambiguity decreases the quality and reliability of information retrieved from digital libraries. Existing methods have tried to solve this problem by predefining a feature set based on expert's knowledge for a specific dataset. In this paper, we propose a new approach which uses deep neural network to learn features automatically from data. Additionally, we propose the general system architecture for author name disambiguation on any dataset. In this research, we evaluate the proposed method on a dataset containing Vietnamese author names. The results show that this method significantly outperforms other methods that use predefined feature set. The proposed method achieves 99.31% in terms of accuracy. Prediction error rate decreases from 1.83% to 0.69%, i.e., it decreases by 1.14%, or 62.3% relatively compared with other methods that use predefined feature set (Table 3).
University of Koblenz-Landau（科布伦茨-兰道大学）；Institute for Web Science and Technologies（网络科学与技术研究所）
As the number of authors is increasing exponentially over years, the number of authors sharing the same names is increasing proportionally. This makes it challenging to assign newly published papers to their adequate authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use a collection from the DBLP repository that contains more than 5 million bibliograp…
University of Michigan（密歇根大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
In author name disambiguation, author forenames are used to decide which name instances are disambiguated together and how much they are likely to refer to the same author. Despite such a crucial role of forenames, their effect on the performances of heuristic (string matching) and algorithmic disambiguation is not well understood. This study assesses the contributions of forenames in author name disambiguation using multiple labeled datasets under varying ratios and lengths of full forenames, reflecting real-world scenarios in which an author is represented by forename variants (synonym) and …
Author name ambiguity in a digital library may affect the findings of research that mines authorship data of the library. This study evaluates author name disambiguation in DBLP, a widely used but insufficiently evaluated digital library for its disambiguation performance. In doing so, this study takes a triangulation approach that author name disambiguation for a digital library can be better evaluated when its performance is assessed on multiple labeled datasets with comparison to baselines. Tested on three types of labeled data containing 5,000 ~ 700K disambiguated names and 6M pairs of dis…
University of Michigan（密歇根大学）；Syracuse University（雪城大学）
In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data. This paper examines how the ratios of negative to positive training data can affect the performance of machine learning algorithms to disambiguate author names in bibliographic records. On multiple labeled datasets, three classifiers - Logistic Regression, Naïve Bayes, and Random Forest - are trained through representative features such as coauthor names, and title words extracted from the same training data but with various positive-negative training d…
Duke University（杜克大学）；American Institutes for Research（美国研究所）
We present PatentsView-Evaluation, a Python package that enables researchers to evaluate the performance of inventor name disambiguation systems such as PatentsView.org. The package includes benchmark datasets and evaluation tools, and aims to advance research on inventor name disambiguation by providing access to high-quality evaluation data and improving evaluation standards.
Indian Institute of Science Education and Research（印度科学教育与研究院）
Author names often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library, and expert discovery. A plethora of techniques for disambiguation of author names has been proposed in the literature. I try to focus on the research efforts targeted to disambiguate author names. I first go through the conventional methods, then I discus…
In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it challenging to assign newly published papers to their respective authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic record…
The disambiguation of author names is an important and challenging task in bibliometrics. We propose an approach that relies on an external source of information for selecting and validating clusters of publications identified through an unsupervised author name disambiguation method. The application of the proposed approach to a random sample of Italian scholars shows encouraging results, with an overall precision, recall, and F-Measure of over 96%. The proposed approach can serve as a starting point for large-scale census of publication portfolios for bibliometric analyses at the level of in…
Author name ambiguity remains a critical open problem in digital libraries due to synonymy and homonymy of names. In this paper, we propose a novel approach to link author names to their real-world entities by relying on their co-authorship pattern and area of research. Our supervised deep learning model identifies an author by capturing his/her relationship with his/her co-authors and area of research, which is represented by the titles and sources of the target author's publications. These attributes are encoded by their semantic and symbolic representations. To this end, Bib2Auth uses ~ 22K…
Adequately disambiguating author names in bibliometric databases is a precondition for conducting reliable analyses at the author level. In the case of bibliometric studies that include many researchers, it is not possible to disambiguate each single researcher manually. Several approaches have been proposed for author name disambiguation but there has not yet been a comparison of them under controlled conditions. In this study, we compare a set of unsupervised disambiguation approaches. Unsupervised approaches specify a model to assess the similarity of author mentions a priori instead of tra…
Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publications and claim those that are theirs. Indirectly, these tools allow for the inexpensive collection of large annotated training data, which can be further leveraged to build a complementary automated disambiguation system capable of inferring patterns for identifying publications…
How can we evaluate the performance of a disambiguation method implemented on big bibliographic data? This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale. This study demonstrates the potential by evaluating the disambiguation performances of Author-ity2009 (which algorithmically disambiguates author names in MEDLINE) using 3 million name instances that are automatically labeled through linkage to 5 million ORCID researcher profiles. Results show that although ORCID-linked labeled data do not effectively represe…
There are a number of solutions that perform unsupervised name disambiguation based on the similarity of bibliographic records or common co-authorship patterns. Whether the use of these advanced methods, which are often difficult to implement, is warranted depends on whether the accuracy of the most basic disambiguation methods, which only use the author's last name and initials, is sufficient for a particular purpose. We derive realistic estimates for the accuracy of simple, initials-based methods using simulated bibliographic datasets in which the true identities of authors are known. Based …
In this paper, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS-AND, two large, gold-standard datasets for author name disambiguation (AND), of which LAGOS-AND-BLOCK is created for clustering-based AND research and LAGOS-AND-PAIRWISE is created for classification-based AND research. Our LAGOS-AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021…
Beihang University（北京航空航天大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Lehigh University（理海大学）
Name disambiguation aims to identify unique authors with the same name. Existing name disambiguation methods always exploit author attributes to enhance disambiguation results. However, some discriminative author attributes (e.g., email and affiliation) may change because of graduation or job-hopping, which will result in the separation of the same author's papers in digital libraries. Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the acad…
With the increasing size of digital libraries it has become a challenge to identify author names correctly. The situation becomes more critical when different persons share the same name (homonym problem) or when the names of authors are presented in several different ways (synonym problem). This paper focuses on homonym names in the computer science bibliography DBLP. The goal of this study is to evaluate a method which uses co-authorship networks and analyze the effect of common names on it. For this purpose we clustered the publications of authors with the same name and measured the effecti…
In this work, we introduce a novel method for entity resolution author disambiguation in bibliographic networks. Such a method is based on a 2-steps network traversal using topological similarity measures for rating candidate nodes. Topological similarity is widely used in the Link Prediction application domain to assess the likelihood of an unknown link. A similarity function can be a good approximation for equality, therefore can be used to disambiguate, basing on the hypothesis that authors with many common co-authors are similar. Our method has experimented on a graph-based representation …
FIZ Karlsruhe - Leibniz Institute of Technology（卡尔斯鲁厄专业信息中心——莱布尼茨技术研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University of Bologna（博洛尼亚大学）
Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available as Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Kno…
University of Michigan（密歇根大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
To train algorithms for supervised author name disambiguation, many studies have relied on hand-labeled truth data that are very laborious to generate. This paper shows that labeled training data can be automatically generated using information features such as email address, coauthor names, and cited references that are available from publication records. For this purpose, high-precision rules for matching name instances on each feature are decided using an external-authority database. Then, selected name instances in target ambiguous data go through the process of pairwise matching based on …
The paper examines extent of bias in the performance rankings of research organisations when the assessments are based on unsupervised author-name disambiguation algorithms. It compares the outcomes of a research performance evaluation exercise of Italian universities using the unsupervised approach by Caron and van Eck (2014) for derivation of the universities' research staff, with those of a benchmark using the supervised algorithm of D'Angelo, Giuffrida, and Abramo (2011), which avails of input data. The methodology developed could be replicated for comparative analyses in other frameworks …
Following the work of Krumov et al. [Eur. Phys. J. B 84, 535 (2011)] we revisit the question whether the usage of large citation datasets allows for the quantitative assessment of social (by means of coauthorship of publications) influence on the progression of science. Applying a more comprehensive and well-curated dataset containing the publications in the journals of the American Physical Society during the whole 20th century we find that the measure chosen in the original study, a score based on small induced subgraphs, has to be used with caution, since the obtained results are highly sen…
An author name disambiguation (AND) algorithm identifies a unique author entity record from all similar or same publication records in scholarly or similar databases. Typically, a clustering method is used that requires calculation of similarities between each possible record pair. However, the total number of pairs grows quadratically with the size of the author database making such clustering difficult for millions of records. One remedy for this is a blocking function that reduces the number of pairwise similarity calculations. Here, we introduce a new way of learning blocking schemes by us…
Several studies have found that collaboration networks are scale-free, proposing that such networks can be modeled by specific network evolution mechanisms like preferential attachment. This study argues that collaboration networks can look more or less scale-free depending on the methods for resolving author name ambiguity in bibliographic data. Analyzing networks constructed from multiple datasets containing 3.4M ~ 9.6M publication records, this study shows that collaboration networks in which author names are disambiguated by the commonly used heuristic, i.e., forename-initial-based name ma…
National exercises for the evaluation of research activity by universities are becoming regular practice in ever more countries. These exercises have mainly been conducted through the application of peer-review methods. Bibliometrics has not been able to offer a valid large-scale alternative because of almost overwhelming difficulties in identifying the true author of each publication. We will address this problem by presenting a heuristic approach to author name disambiguation in bibliometric datasets for large-scale research assessments. The application proposed concerns the Italian universi…
Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial-based disambiguation has been justified by the assumption that such errors would not affect research findings too much. This paper tests this assumption by analyzing coauthorship networks from five academic fields - biology, computer science, nanoscience, neuroscience, and physics - and an interdisciplinary journal, PNAS. Name instances in datasets of this study were disambigu…
Author name disambiguation results are often evaluated by measures such as Cluster-F, K-metric, Pairwise-F, Splitting & Lumping Error, and B-cubed. Although these measures have distinctive evaluation schemes, this paper shows that they can be calculated in a single framework by a set of common steps that compare truth and predicted clusters through two hash tables recording information about name instances with their predicted cluster indices and frequencies of those indices per truth cluster. This integrative calculation reduces greatly calculation runtime, which is scalable to a clustering t…
Author name ambiguity causes inadequacy and inconvenience in academic information retrieval, which raises the necessity of author name disambiguation (AND). Existing AND methods can be divided into two categories: the models focusing on content information to distinguish whether two papers are written by the same author, the models focusing on relation information to represent information as edges on the network and to quantify the similarity among papers. However, the former requires adequate labeled samples and informative negative samples, and are also ineffective in measuring the high-orde…
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 disp…
We present a novel algorithm and validation method for disambiguating author names in very large bibliographic data sets and apply it to the full Web of Science (WoS) citation index. Our algorithm relies only upon the author and citation graphs available for the whole period covered by the WoS. A pair-wise publication similarity metric, which is based on common co-authors, self-citations, shared references and citations, is established to perform a two-step agglomerative clustering that first connects individual papers and then merges similar clusters. This parameterized model is optimized usi…
Patent data represent a significant source of information on innovation and the evolution of technology through networks of citations, co-invention and co-assignment of new patents. A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in the creation of a technology. In this paper, we describe a new algorithm that uses high-resolution geolocation to disambiguate both inventor and assignees on more than 3.6 million patent…
The study of science at the individual micro-level frequently requires the disambiguation of author names. The creation of author's publication oeuvres involves matching the list of unique author names to names used in publication databases. Despite recent progress in the development of unique author identifiers, e.g., ORCID, VIVO, or DAI, author disambiguation remains a key problem when it comes to large-scale bibliometric analysis using data from multiple databases. This study introduces and validates a new methodology called seed+expand for semi-automatic bibliographic data collection for a…
We investigate how author name homonymy distorts clustered large-scale co-author networks, and present a simple, effective, scalable and generalizable algorithm to ameliorate such distortions. We evaluate the performance of the algorithm to improve the resolution of mesoscopic network structures. To this end, we establish the ground truth for a sample of author names that is statistically representative of different types of nodes in the co-author network, distinguished by their role for the connectivity of the network. We finally observe that this distinction of node roles based on the mesosc…
As digital collections of scientific literature are widespread and used frequently in knowledge-intense working environments, it has become a challenge to identify author names correctly. The treatment of homonyms is crucial for the reliable resolution of author names. Apart from varying handling of first, middle and last names, vendors as well as the digital library community created tools to address the problem of author name disambiguation. This technical report focuses on two widespread collections of scientific literature, Web of Science (WoS) and Scopus, and the coverage with author iden…
In this paper we describe a case study where researchers in the social sciences (n=19) assess topical relevance for controlled search terms, journal names and author names which have been compiled automatically by bibliometric-enhanced information retrieval (IR) services. We call these bibliometric-enhanced IR services Search Term Recommender (STR), Journal Name Recommender (JNR) and Author Name Recommender (ANR) in this paper. The researchers in our study (practitioners, PhD students and postdocs) were asked to assess the top n pre-processed recommendations from each recommender for specific …
I describe a method to separate the articles of different authors with the same name. It is based on a distance between any two publications, defined in terms of the probability that they would have as many coincidences if they were drawn at random from all published documents. Articles with a given author name are then clustered according to their distance, so that all articles in a cluster belong very likely to the same author. The method has proven very useful in generating groups of papers that are then selected manually. This simplifies considerably citation analysis when the author publi…
As a social science information service for the German language countries, we document research projects, publications, and data in relevant fields. At the same time, we aim to provide well-founded bibliometric studies of these fields. Performing a citation analysis on an area of the German social sciences is, however, a serious challenge given the low and likely significantly biased coverage of these fields in the standard citation databases. Citations, and especially author citations, play a highly significant role in that literature, however. In this work in progress, we report preliminary …
Algorithmic historiography was proposed by Eugene Garfield in collaboration with Irving Sher in the 1960s, but further developed only recently into HistCite^{TM} with Alexander Pudovkin. As in history writing, HistCite^{TM} reconstructs by drawing intellectual lineages. In addition to cited references, however, documents can be attributed a multitude of other variables such as title words, keywords, journal names, author names, and even full texts. New developments in multidimensional scaling (MDS) enable us not only to visualize these patterns at each moment of time, but also to animate them …
Term suggestion or recommendation modules can help users to formulate their queries by mapping their personal vocabularies onto the specialized vocabulary of a digital library. While we examined actual user queries of the social sciences digital library Sowiport we could see that nearly one third of the users were explicitly looking for author names rather than terms. Common term recommenders neglect this fact. By picking up the idea of polyrepresentation we could show that in a standardized IR evaluation setting we can significantly increase the retrieval performances by adding topical-relate…
Revisiting gender bias research in bibliometrics: Standardizing methodological variability using Scholarly Data Analysis (SoDA) Cards
School of Information Sciences, University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校信息科学学院）；School of Information, University of Michigan - Ann Arbor（密歇根大学安娜堡分校信息学院）；School of Social Sciences and Technology, Technical University of Munich（慕尼黑工业大学社会科学与技术学院）
There are different ways in which the authors of a scientific publication can determine the order in which their names are listed. Sometimes author names are simply listed alphabetically. In other cases, authorship order is determined based on the contribution authors have made to a publication. Contribution-based authorship can facilitate proper credit assignment, for instance by giving most credits to the first author. In the case of alphabetical authorship, nothing can be inferred about the relative contribution made by the different authors of a publication. In this paper, we present an em…
Any Old Tom, Dick or Harry: The Citation Impact of First Name Genderedness
Southern University of Denmark（南丹麦大学）；École de Bibliothéconomie et des Sciences de l’Information, Université de Montréal（蒙特利尔大学图书信息学院）；Université du Québec à Montréal（魁北克大学蒙特利尔分校）；Consortium Érudit, Université de Montréal（蒙特利尔大学Érudit联盟）；Observatoire des Sciences et des Technologies, Centre Interuniversitaire de Recherche sur la Science et la Technologie, Université du Québec à Montréal（魁北克大学蒙特利尔分校跨大学科研中心科学与技术观测站）
本研究通过融合维基数据名字性别化表与Web of Science索引的2010-2019年美国作者论文数据，分析名字性别化与引用分布的关系，发现女性化和中性名字在所有学科和作者角色中普遍存在引用劣势。
