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Building a Simple COVID-19 Knowledge Graph in Bahasa Indonesia: A Preliminary Study
  • Arief Purnama Muharram,
  • Farhan Hilmi Taufikulhakim,
  • Ayu Purwarianti
Arief Purnama Muharram
Institut Teknologi Bandung

Corresponding Author:[email protected]

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Farhan Hilmi Taufikulhakim
Universitas Indonesia
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Ayu Purwarianti
Institut Teknologi Bandung
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COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development.
08 Mar 2024Submitted to TechRxiv
15 Mar 2024Published in TechRxiv
09 Nov 2023Published in 2023 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC). 10.1109/IBITeC59006.2023.10390908