Author Name Disambiguation Research Articles

PubMed Computed Authors in 2024: an open resource of disambiguated author names in biomedical literature.

Over 55% of author names in PubMed are ambiguous: the same name is shared by different individual researchers. This poses significant challenges on precise literature retrieval for author name queries, a common behavior in biomedical literature search. In response, we present a comprehensive dataset of disambiguated authors. Specifically, we complement the automatic PubMed Computed Authors algorithm with the latest ORCID data for improved accuracy. As a result, the enhanced algorithm achieves high performance in author name disambiguation, and subsequently our dataset contains more than 21 million disambiguated authors for over 35 million PubMed articles and is incrementally updated on a weekly basis. More importantly, we make the dataset publicly available for the community such that it can be utilized in a wide variety of potential applications beyond assisting PubMed's author name queries. Finally, we propose a set of guidelines for best practices of authors pertaining to use of their names. The PubMed Computed Authors dataset is publicly available for bulk download at: https://ftp.ncbi.nlm.nih.gov/pub/lu/ComputedAuthors/. Additionally, it is available for query through web API at: https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/authors/. Supplementary data are available at Bioinformatics online.

Towards Effective Author Name Disambiguation by Hybrid Attention

Towards Effective Author Name Disambiguation by Hybrid Attention

Bridging the gap in author names: building an enhanced author name dataset for biomedical literature system.

Author name incompleteness, referring to only first initial available instead of full first name, is a long-standing problem in MEDLINE and has a negative impact on biomedical literature systems. The purpose of this study is to create an Enhanced Author Names (EAN) dataset for MEDLINE that maximizes the number of complete author names. The EAN dataset is built based on a large-scale name comparison and restoration with author names collected from multiple literature databases such as MEDLINE, Microsoft Academic Graph, and Semantic Scholar. We assess the impact of EAN on biomedical literature systems by conducting comparative and statistical analyses between EAN and MEDLINE's author names dataset (MAN) on 2 important tasks, author name search and author name disambiguation. Evaluation results show that EAN improves the number of full author names in MEDLINE from 69.73 million to 110.9 million. EAN not only restores a substantial number of abbreviated names prior to the year 2002 when the NLM changed its author name indexing policy but also improves the availability of full author names in articles published afterward. The evaluation of the author name search and author name disambiguation tasks reveal that EAN is able to significantly enhance both tasks compared to MAN. The extensive coverage of full names in EAN suggests that the name incompleteness issue can be largely mitigated. This has significant implications for the development of an improved biomedical literature system. EAN is available at https://zenodo.org/record/10251358, and an updated version is available at https://zenodo.org/records/10663234.

Automatic author name disambiguation by differentiable feature selection

Author name disambiguation (AND) is the task of resolving the ambiguity problem in bibliographic databases, where distinct real-world authors may share the same name or same author may have distinct names. The aim of AND is to split the name-ambiguous entities (articles) into the corresponding authors. Existing AND algorithms mainly focus on designing different similarity metrics between two ambiguous articles. However, most previous methods empirically select and process the features of entities, then use features to predict the similarity by data-driven models. In this article, we are motivated by natural questions: Which features are most useful for splitting name-ambiguous entities? Can they be automatically determined by an optimisation approach rather than heuristic feature engineering? Therefore, we proposed a novel end-to-end differentiable feature selection algorithm, automatically searching the optimal features for AND task (AAND). AAND optimises the discrete feature selection by differentiable Gumbel-Softmax, leading to the joint learning of feature selection policy and similarity prediction model. The experiments are conducted on a benchmark data set, S2AND, which harmonises eight different AND data sets. The results show that the performance of our proposal is superior to the advanced AND methods and feature selection algorithms. Meanwhile, deep insights into AND features are also given.

Graph-based methods for Author Name Disambiguation: a survey.

Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers' activity. Various issues, such as homonymy, scarcity of contextual information, and cardinality of the SKG, make simple name string matching insufficient or computationally complex. Many AND deep learning methods have been developed, and interesting surveys exist in the literature, comparing the approaches in terms of techniques, complexity, performance, etc. However, none of them specifically addresses AND methods in the context of SKGs, where the entity-relationship structure can be exploited. In this paper, we discuss recent graph-based methods for AND, define a framework through which such methods can be confronted, and catalog the most popular datasets and benchmarks used to test such methods. Finally, we outline possible directions for future work on this topic.

Author Name Disambiguation Based on Heterogeneous Graph

<p>Since multiple people share the same name in the real world, this will cause performance degradation to academic search systems and lead to misattribution of publications. The author name disambiguation algorithm has not yet to be well solved. In this paper, we propose a disambiguation method that combines heterogeneous graph-based and improved label propagation, first we construct a publication heterogeneous graph network, then graph neural networks is applied to aggregate the nodes representation and relation types, finally combined with the improved label propagation algorithm to realize clustering. The task of author name disambiguation is completed to improve the retrieval performance. Experimental results on two public datasets show that our method was improved by 2.8% and 4.9% over the suboptimal method, respectively. Our method can effectively reduce the number of publications returning the wrong author and improve the performance of the academic retrieval system.</p> <p> </p>

Deep author name disambiguation using DBLP data

In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation 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 records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.

CluEval: A Python tool for evaluating clustering performance in named entity disambiguation

CluEval is a Python tool designed to assess the accuracy of named entity disambiguation methods in clustering performance. It allows users to employ five commonly used clustering evaluation metrics in entity disambiguation research. With newly developed, comprehensive, and fast algorithms, CluEval can handle large-scale computations and help users better understand and systematically interpret the clustering performance of named entity disambiguation methods. This tool can serve as a stand-alone evaluation code or be integrated as a module into any named entity disambiguation framework that produces clusters as the final disambiguation outputs, such as author name disambiguation.

PubExN: An Automated PubMed Bulk Article Extractor with Affiliation Normalization Package.

Biomedical article extraction is the preliminary step for every biomedical application. These applications are helpful in finding the gene, disease, chemical, drugs, protein entities. Finding entities relation such as gene-gene entities, drug-disease interaction, and chemical protein relation the PubExN can be helpful for these types of biomedical applications. In most cases, domain experts do this extraction process on their own. Human interference makes this process time-consuming and there is a high probability, that documents can be missed during the extraction process. To get rid of these complicated processes a python package is introduced to automate the process of bulk extraction from the PubMed database. The extraction process covers all the citation information with the associated abstract. The batch approach is used to extract the bulk extraction. The motivation for the development of PubExN was to provide flexibility for the extraction process of biomedical article's text data from NCBI's PubMed database. Basically, NCBI's PubMed database article contains the article id or can say PubMed-id (PMID), the title of the article, abstract, authors information, etc. This package will benefit many biomedical texts mining research including biomedical named entity recognition, biomedical relation extraction, literature discovery, knowledgebase creation, and various biomedical Natural Language Processing (NLP) tasks. In addition, it could be used in the author name disambiguation problems and new drug discoveries. This package will help save time and extra effort for the extraction and normalization process of PubMed articles.

Deep Author Name Disambiguation using DBLP Data

Deep Author Name Disambiguation using DBLP Data

LAGOS‐AND: A large gold standard dataset for scholarly author name disambiguation

AbstractIn this article, 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 sub‐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) include 7.5 M citations authored by 798 K unique authors (LAGOS‐AND‐BLOCK) and close to 1 M instances (LAGOS‐AND‐PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available.

MORE: Toward Improving Author Name Disambiguation in Academic Knowledge Graphs

MORE: Toward Improving Author Name Disambiguation in Academic Knowledge Graphs

A knowledge graph embeddings based approach for author name disambiguation using literals

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 in the form of 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 Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: (1) multimodal KGEs, (2) a blocking procedure, and finally, (3) hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8–14% in terms of F1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github (https://github.com/sntcristian/and-kge) and Zenodo (https://doi.org/10.5281/zenodo.6309855) respectively.

Online author name disambiguation in evolving digital library

Name ambiguity is a prevalent problem in digital library domain where mapping of bibliographic records to authors is a major issue. The unprecedented growth of the bibliographic records and absence of unique identifiers are further exacerbating the problem. Specifically, name ambiguity affects various bibliometric analysis tasks that include record management as well as scientific assessment of the authors thereby necessitating the name disambiguation. The name disambiguation task is to assign the records, possibly with the ambiguous authorship, to corresponding authors. While existing techniques are good at extracting abstract features from set of records with a common author name that can be subsequently used for clustering the records based on unique author identities, however, such techniques usually perform poorly in disambiguating isolated individual record entries that arrive continuously. Disambiguation of only newly arrived records, rather than the whole records of the digital library is challenging, however, computationally rewarding and thus, not only preferable but becoming the necessity due to tremendous growth in the number of bibliographic records with the time, which is likely to continue. In this regard, we propose an online author name disambiguation approach for evolving digital library. Our approach involves representation learning of records in an online manner in evolving (academic networks) digital library using dynamic graph embedding and clustering of latent representation of records. We show the use of our online name disambiguation method in batch setting (for static or initial records of digital library) and incremental setting (for new records of digital library). Significant improvement, over existing state-of-the-art methods in terms of various evaluation metrics, has been observed which indicates the effectiveness of the proposed approach.

The Microsoft Academic Knowledge Graph enhanced: Author name disambiguation, publication classification, and embeddings

Abstract Although several large knowledge graphs have been proposed in the scholarly field, such graphs are limited with respect to several data quality dimensions such as accuracy and coverage. In this article, we present methods for enhancing the Microsoft Academic Knowledge Graph (MAKG), a recently published large-scale knowledge graph containing metadata about scientific publications and associated authors, venues, and affiliations. Based on a qualitative analysis of the MAKG, we address three aspects. First, we adopt and evaluate unsupervised approaches for large-scale author name disambiguation. Second, we develop and evaluate methods for tagging publications by their discipline and by keywords, facilitating enhanced search and recommendation of publications and associated entities. Third, we compute and evaluate embeddings for all 239 million publications, 243 million authors, 49,000 journals, and 16,000 conference entities in the MAKG based on several state-of-the-art embedding techniques. Finally, we provide statistics for the updated MAKG. Our final MAKG is publicly available at https://makg.org and can be used for the search or recommendation of scholarly entities, as well as enhanced scientific impact quantification.

Exploiting Higher Order Multi-dimensional Relationships with Self-attention for Author Name Disambiguation

Name ambiguity is a prevalent problem in scholarly publications due to the unprecedented growth of digital libraries and number of researchers. An author is identified by their name in the absence of a unique identifier. The documents of an author are mistakenly assigned due to underlying ambiguity, which may lead to an improper assessment of the author. Various efforts have been made in the literature to solve the name disambiguation problem with supervised and unsupervised approaches. The unsupervised approaches for author name disambiguation are preferred due to the availability of a large amount of unlabeled data. Bibliographic data contain heterogeneous features, thus recently, representation learning-based techniques have been used in literature to embed heterogeneous features in common space. Documents of a scholar are connected by multiple relations. Recently, research has shifted from a single homogeneous relation to multi-dimensional (heterogeneous) relations for the latent representation of document. Connections in graphs are sparse, and higher order links between documents give an additional clue. Therefore, we have used multiple neighborhoods in different relation types in heterogeneous graph for representation of documents. However, different order neighborhood in each relation type has different importance which we have empirically validated also. Therefore, to properly utilize the different neighborhoods in relation type and importance of each relation type in the heterogeneous graph, we propose attention-based multi-dimensional multi-hop neighborhood-based graph convolution network for embedding that uses the two levels of an attention, namely, (i) relation level and (ii) neighborhood level, in each relation. A significant improvement over existing state-of-the-art methods in terms of various evaluation matrices has been obtained by the proposed approach.

Identification of Indonesian Authors Using Deep Neural Networks

Author Name Disambiguation (AND) is a problem that occurs when a set of publications contains ambiguous names of authors, i.e. the same author may appear with different names (synonyms) in other published papers, or author (authors) who may be different who may have the same name (homonym). In this final project, we will design a model with a Deep Neural Network (DNN) classifier. The dataset used in this final project uses primary data sourced from the Scopus website. This research focuses on integrating data from Indonesian authors. Parameters accuracy, sensitivity and precision are standard benchmarks to determine the performance of the method used to solve AND problems. The best DNN classification model achieves 99.9936% Accuracy, 93.1433% Sensitivity, 94.3733% Precision. Then for the highest performance measurement, the case of Non Synonym-Homonym (SH) has 99.9967% Accuracy, 96.7388% Sensitivity, and 97.5102% Precision.

Completing features for author name disambiguation (AND): an empirical analysis

Completing features for author name disambiguation (AND): an empirical analysis

Toward a New Paradigm for Author Name Disambiguation

Author Name Disambiguation (AND) has emerged as a significant challenge in the bibliometric context with the growing volume of scientific literature. When citations written by different authors have the same names (polysemy or homonym names), and when an author has different names, there is ambiguity (synonyms or name variants). It is difficult to associate a citation with the correct author. Polysemy and synonyms cause merging and splitting anomalies in the citations. These anomalies affect the quantification of an author’s productivity (bibliometric analysis) and the reliability and quality of the information retrieved. Many techniques for AND have been proposed in the literature; most of them do not go beyond string matching or text matching. Most do not consider the context or semantics of the terms used in the citations. The AND problem is resolved semantically in this paper using the deep learning technique on the PubMed dataset. The experimental results show that the proposed method achieves overall (11.72%, 12.5%, and 12.1%) higher precision, recall, and f-measure than the pairwise class classification.

The data set knowledge graph: Creating a linked open data source for data sets

Abstract Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather on associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.