Author name disambiguation is the process of disambiguation and record linkage applied to the names of individual people. The process could, for example, distinguish individuals with the name "John Smith".
An editor may apply the process to scholarly documents where the goal is to find all mentions of the same author and cluster them together. Authors of scholarly documents often share names which makes it hard to distinguish each author's work. Hence, author name disambiguation aims to find all publications that belong to a given author and distinguish them from publications of other authors who share the same name.
Considerable research has been conducted into name disambiguation. [1] [2] [3] [4] [5] Typical approaches for author name disambiguation rely on information to distinguish between authors, including (but not limited to) information about the authors such as: their name representation, affiliations and email addresses, and information about the publication: such as year of publication, co-authors, and the topic of the paper. This information can be used to train a machine learning classifier to decide whether two author mentions refer to the same author or not. [6] Much research regards name disambiguation as a clustering problem, i.e., partitioning documents into clusters, where each represents an author. [2] [7] [8] Other research treats it as a classification problem. [9] Some works constructs a document graph and utilizes the graph topology to learn document similarity. [8] [10] Recently, several pieces of research [10] [11] aim to learn low-dimensional document representations by employing network embedding methods. [12] [13]
There are multiple reasons that cause author names to be ambiguous, among which: individuals may publish under multiple names for a variety of reasons including different transliteration, misspelling, name change due to marriage, or the use of nicknames or middle names and initials. [14]
Motivations for disambiguating individuals include identifying inventors from patents, and researchers across differing publishers, research institutions and time periods. [15] Name disambiguation is also a cornerstone in author-centric academic search and mining systems, such as AMiner (formerly ArnetMiner). [16]
Author name disambiguation is only one record linkage problem in the scholarly data domain. Closely related, and potentially mutually beneficial problems include: organisation (affiliation) disambiguation, [17] as well as conference or publication venue disambiguation, since data publishers often use different names or aliases for these entities.
Several well-known benchmarks to evaluate author name disambiguation are listed below, each of which provides publications with some ambiguous names and their ground truths.
Source Codes
Biclustering, block clustering, Co-clustering or two-mode clustering is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix. The term was first introduced by Boris Mirkin to name a technique introduced many years earlier, in 1972, by John A. Hartigan.
SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference.
Expertise finding is the use of tools for finding and assessing individual expertise. In the recruitment industry, expertise finding is the problem of searching for employable candidates with certain required skills set. In other words, it is the challenge of linking humans to expertise areas, and as such is a sub-problem of expertise retrieval.
Hans-Peter Kriegel is a German computer scientist and professor at the Ludwig Maximilian University of Munich and leading the Database Systems Group in the Department of Computer Science. He was previously professor at the University of Würzburg and the University of Bremen after habilitation at the Technical University of Dortmund and doctorate from Karlsruhe Institute of Technology.
AMiner is a free online service used to index, search, and mine big scientific data.
Jie Tang is a full-time professor at the Department of Computer Science of Tsinghua University. He received a PhD in computer science from the same university in 2006. He is known for building the academic social network search system AMiner, which was launched in March 2006 and now has attracted 2,766,356 independent IP accesses from 220 countries. His research interests include social networks and data mining.
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Philip S. Yu is an American computer scientist and professor of information technology at the University of Illinois at Chicago. He is a prolific author, holds over 300 patents, and is known for his work in the field of data mining.
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Arthur Zimek is a professor in data mining, data science and machine learning at the University of Southern Denmark in Odense, Denmark.
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Wei Wang is a Chinese-born American computer scientist. She is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles and the director of the Scalable Analytics Institute (ScAi). Her research specializes in big data analytics and modeling, database systems, natural language processing, bioinformatics and computational biology, and computational medicine.
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Jiliang Tang is a Chinese-born computer scientist and associate professor at Michigan State University in the Computer Science and Engineering Department, where he is the director of the Data Science and Engineering (DSE) Lab. His research expertise is in data mining and machine learning.
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Yixin Chen is a computer scientist, academic, and author. He is a professor of computer science and engineering at Washington University in St. Louis.
Nitesh V. Chawla is a computer scientist and data scientist currently serving as the Frank M. Freimann Professor of Computer Science and Engineering at the University of Notre Dame. He is the Founding Director of the Lucy Family Institute for Data & Society. Chawla's research expertise lies in machine learning, data science, and network science. He is also the co-founder of Aunalytics, a data science software and cloud computing company. Chawla is a Fellow of the: American Association for the Advancement of Sciences (AAAS), Association for Computing Machinery (ACM), Association for the Advancement of Artificial Intelligence, Asia Pacific Artificial Intelligence Association, and Institute of Electrical and Electronics Engineers (IEEE). He has received multiple awards, including the 1st Source Bank Commercialization Award in 2017, Outstanding Teaching Award (twice), IEEE CIS Early Career Award, National Academy of Engineering New Faculty Award, and the IBM Big Data Award in 2013. One of Chawla's most recognized publications, with a citation count of over 30,000, is the research paper titled "SMOTE: Synthetic Minority Over-sampling Technique." Chawla's research has garnered a citation count of over 65,000 and an H-index of 81.
Xing Xie is a partner research manager at Microsoft Research Asia. As a computer scientist, his research has focused on data mining, social computing, and responsible AI. He has published more than 400 papers which have been cited more than 60,000 times. He has been on organizing committees or helped with the programs of over 70 conferences and workshops.