Abstract

In the digital era, search engines such as Google Scholar make it easier to access scientific information, but there are challenges in determining the relevance and accuracy of articles. One technique used to select scientific articles relevant to a particular topic is entity matching. This study aims to analyze the performance of entity matching using four models owned by DeepMatcher. The models include Attention, Hybrid, RNN, and SIF, which are applied to a dataset of articles with keyword fairness in AI. The collected dataset consisted of 50 articles retrieved from Publish or Perish 8. The dataset was preprocessed up to pairing, resulting in a final dataset of 1225 rows and eight columns. The columns follow the column types that must be present in the dataset that will be processed by DeepMatcher, namely the "left,""right,""label,"and "ID"attributes. Based on the results of the analysis, it was found that the most suitable DeepMatcher model, namely the RNN model, had the highest average F1 score of 54.08%.

Original languageEnglish
Title of host publicationProceedings - IEIT 2023
Subtitle of host publication2023 International Conference on Electrical and Information Technology
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages120-125
Number of pages6
ISBN (Electronic)9798350327298
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Electrical and Information Technology, IEIT 2023 - Malang, Indonesia
Duration: 14 Sept 202315 Sept 2023

Publication series

NameProceedings - IEIT 2023: 2023 International Conference on Electrical and Information Technology

Conference

Conference2023 International Conference on Electrical and Information Technology, IEIT 2023
Country/TerritoryIndonesia
CityMalang
Period14/09/2315/09/23

Keywords

  • Attention
  • DeepMatcher
  • Entity Matching
  • Hybrid
  • Recurrent Neural Network (RNN)
  • Smooth Inverse Frequency (SIF)

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