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

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

COVID-19; deep learning; fact-checking; natural language inference; knowledge graph; natural language.

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.

Arief Purnama Muharram & Ayu Purwarianti

DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.1.2

IRCS-ITB

13 August 2025

Sri Wahyuni

ISSN-2337-5787

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