TELKOMNIKA Telecommunication, Computing, Electronics and Control
Author identification in bibliographic data using deep neural networks
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Author identification in bibliographic data using deep neural networks
Author identification in bibliographic data using deep neural networks
Subject
Author name disambiguation
Bibliographic data
Deep neural networks
Homonym
Synonym
Bibliographic data
Deep neural networks
Homonym
Synonym
Description
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.
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.
Creator
Firdaus, Siti Nurmaini, Reza Firsandaya Malik, Annisa Darmawahyuni, Muhammad Naufal Rachmatullah, Andre Herviant Juliano, Tio Artha Nugraha, Varindo Ockta Keneddi Putra
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Aug 31, 2020
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Firdaus, Siti Nurmaini, Reza Firsandaya Malik, Annisa Darmawahyuni, Muhammad Naufal Rachmatullah, Andre Herviant Juliano, Tio Artha Nugraha, Varindo Ockta Keneddi Putra, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Author identification in bibliographic data using deep neural networks,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3875.
Author identification in bibliographic data using deep neural networks,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3875.