Author name disambiguation is the process of
disambiguation
Word-sense disambiguation is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious.
Given that natural language requires ref ...
and
record linkage
Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and d ...
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.
Methods
Considerable research has been conducted into name disambiguation.
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
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...
classifier to decide whether two author mentions refer to the same author or not. Much research regards name disambiguation as a
clustering problem, i.e., partitioning documents into clusters, where each represents an author.
Other research treats it as a classification problem.
Some works construct a document graph and utilize the graph topology to learn document similarity.
Recently, several pieces of research
aim to learn low-dimensional document representations by employing network embedding methods.
Applications
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.
Motivations for disambiguating individuals include identifying inventors from patents, and researchers across differing publishers, research institutions and time periods. Name disambiguation is also a cornerstone in author-centric academic search and mining systems, such as
AMiner
AMiner (formerly ArnetMiner) is a free online service used to index, search, and mine big scientific data.
Overview
AMiner (ArnetMiner) is designed to search and perform data mining operations against academic publications on the Internet, usin ...
(formerly ArnetMiner).
Similar issues
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, as well as conference or publication venue disambiguation, since data publishers often use different names or aliases for these entities.
See also
*
Resources
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.
AMiner name disambiguation datasetCiteSeerX name disambiguation datasetSemantic Scholar Author Name Disambiguation (S2AND) datasetref>
Source Codes
BeardName disambiguation in AMinerref name="zhang2018name"/>
References
{{reflist
Word-sense disambiguation
Library cataloging and classification
Metadata
Data management