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Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language

Journal of ICT Research and Applications (JICTRA) · Vol. 19 No. 1 (2025) · pp. 27–46

DOI
10.5614/itbj.ict.res.appl.2025.19.1.2
ISSN
2338-5499, 2337-5787
Published
2025-09-15
Publisher
DRI - ITB
Language
eng

Abstract

Automated fact-checking is a key strategy to overcome the spread of COVID-19 misinformation on the internet. These systems typically leverage deep learning approaches through natural language inference (NLI) to verify the truthfulness of information based on supporting evidence. However, one challenge that arises in deep learning is performance stagnation due to a lack of knowledge during training. This study proposes using a knowledge graph (KG) as external knowledge to enhance NLI performance for automated COVID-19 fact-checking in the Indonesian language. The proposed model architecture comprises three modules: a fact module, an NLI module, and a classifier module. The fact module processes information from the KG, while the NLI module handles semantic relationships between the given premise and hypothesis. The representation vectors from both modules are concatenated and fed into the classifier module to produce the final result. The model was trained using the generated Indonesian COVID-19 fact-checking dataset and the COVID-19 KG Bahasa Indonesia. Our study demonstrates that incorporating KGs can significantly improve NLI performance in fact-checking, achieving a maximum accuracy of 0.8616. This suggests that KGs are a valuable component for enhancing NLI performance in automated fact-checking.

Keywords

Collaboration network of the authors

19 authors · 30 co-authorship links · 6 across different organisations

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  • Bandung Institute of Technology
  • Telkom University
  • Badan Pusat Statistik
  • Cohere (Canada)
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dwi Hendratmo WidyantoroBandung Institute of Technology2
Aswin JuariBandung Institute of Technology1
Novi YuslianiBandung Institute of Technology1
Hari Bagus FirdaudAffiliation unknown1
Zakiy Firdaus AlfikriBandung Institute of Technology1
Untari Novia WisestyTelkom University1
Tati Rajab MengkoBandung Institute of Technology1
Adi PancoroBandung Institute of Technology1
Afrida HelenBandung Institute of Technology1
Nila NurmalaBadan Pusat Statistik1
Fariska Zakhralativa RuskandaBandung Institute of Technology1
Ubaidillah Ariq PrathamaBandung Institute of Technology1
Samuel CahyawijayaCohere (Canada)1
Nugraha Priya UtamaBandung Institute of Technology1
Muh. Hafizh IzzaturrahimAffiliation unknown1
Ida Bagus Peling PrayogaAffiliation unknown1
Andika Naufal HilmyAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated  Fact-Checking in Indonesian Language</dc:title>
	<dc:creator>Muharram, Arief Purnama</dc:creator>
	<dc:creator>Purwarianti, Ayu</dc:creator>
	<dc:subject xml:lang="en-US">COVID-19</dc:subject>
	<dc:subject xml:lang="en-US">deep learning</dc:subject>
	<dc:subject xml:lang="en-US">fact-checking</dc:subject>
	<dc:subject xml:lang="en-US">natural language inference</dc:subject>
	<dc:subject xml:lang="en-US">knowledge graph</dc:subject>
	<dc:subject xml:lang="en-US">natural language</dc:subject>
	<dc:description xml:lang="en-US">Automated fact-checking is a key strategy to overcome the spread of COVID-19 misinformation on the internet. These systems typically leverage deep learning approaches through natural language inference (NLI) to verify the truthfulness of information based on supporting evidence. However, one challenge that arises in deep learning is performance stagnation due to a lack of knowledge during training. This study proposes using a knowledge graph (KG) as external knowledge to enhance NLI performance for automated COVID-19 fact-checking in the Indonesian language. The proposed model architecture comprises three modules: a fact module, an NLI module, and a classifier module. The fact module processes information from the KG, while the NLI module handles semantic relationships between the given premise and hypothesis. The representation vectors from both modules are concatenated and fed into the classifier module to produce the final result. The model was trained using the generated Indonesian COVID-19 fact-checking dataset and the COVID-19 KG Bahasa Indonesia. Our study demonstrates that incorporating KGs can significantly improve NLI performance in fact-checking, achieving a maximum accuracy of 0.8616. This suggests that KGs are a valuable component for enhancing NLI performance in automated fact-checking.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2025-09-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/24157</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2025.19.1.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 19 No. 1 (2025); 27-46</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2025.19.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/24157/7127</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Journal of ICT Research and Applications</dc:rights>
</oai_dc:dc>