"Needless to Say My Proposal Was Turned Down": The Early Days of Commercial Citation Indexing, an "Error-making" Activity and Its Repercussions Till Today

In today’s neoliberal audit cultures university rankings, quantitative evaluation of publications by JIF or researchers by h-index are believed to be indispensable instruments for “quality assurance” in the sciences. Yet there is increasing resistance against “impactitis” and “evaluitis”. Usually overseen: Trivial errors in Thomson Reuters’ citation indexes produce severe non-trivial effects: Their victims are authors, institutions, journals with names beyond the ASCII-code and scholars of humanities and social sciences. Analysing the “Joshua Lederberg Papers” I want to illuminate eventually successful ‘invention’ of science citation indexing is a product of contingent factors. To overcome severe resistance Eugene Garfield, the “father” of citation indexing, had to foster overoptimistic attitudes and to downplay the severe problems connected to global and multidisciplinary citation indexing. The difficulties to handle different formats of references and footnotes, non-Anglo-American names, and of publications in non-English languages were known to the pioneers of citation indexing. Nowadays the huge for-profit North-American media corporation Thomson Reuters is the owner of the citation databases founded by Garfield. Thomson Reuters’ influence on funding decisions, individual careers, departments, universities, disciplines and countries is immense and ambivalent. Huge technological systems show a heavy inertness. This insight of technology studies is applicable to the large citation indexes by Thomson Reuters, too

Scale‐free collaboration networks: An author name disambiguation perspective

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149559/1/asi24158.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149559/2/asi24158_am.pd

Author identification in bibliographic data using deep neural networks

Author name disambiguation (AND) is a challenging task for scholars who mine bibliographic information for scientific knowledge. A constructive approach for resolving name ambiguity is to use computer algorithms to identify author names. Some algorithm-based disambiguation methods have been developed by computer and data scientists. Among them, supervised machine learning has been stated to produce decent to very accurate disambiguation results. This paper presents a combination of principal component analysis (PCA) as a feature reduction and deep neural networks (DNNs), as a supervised algorithm for classifying AND problems. The raw data is grouped into four classes, i.e., synonyms, homonyms, homonyms-synonyms, and non-homonyms-synonyms classification. We have taken into account several hyperparameters tuning, such as learning rate, batch size, number of the neuron and hidden units, and analyzed their impact on the accuracy of results. To the best of our knowledge, there are no previous studies with such a scheme. The proposed DNNs are validated with other ML techniques such as Naïve Bayes, random forest (RF), and support vector machine (SVM) to produce a good classifier. By exploring the result in all data, our proposed DNNs classifier has an outperformed other ML technique, with accuracy, precision, recall, and F1-score, which is 99.98%, 97.98%, 97.86%, and 99.99%, respectively. In the future, this approach can be easily extended to any dataset and any bibliographic records provider

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