Implementation of Knowledge-Based Graph Neural Networks for Reasoning and Ranking Medical Entities from CORD-19 Texts

Agus Rahmat Fadillah; Irwansyah Saputra

doi:10.56741/jnest.v4i02.1049

Implementation of Knowledge-Based Graph Neural Networks for Reasoning and Ranking Medical Entities from CORD-19 Texts

Authors

Agus Rahmat Fadillah Nusa Mandiri University
Irwansyah Saputra Universitas Nusa Mandiri

DOI:

https://doi.org/10.56741/jnest.v4i02.1049

Keywords:

Biomedical Information Retrieval, Graph Neural Networks, Knowledge Graph, Named Entity Recognition, CORD-19

Abstract

The rapid growth of biomedical literature, espe- cially during the COVID-19 pandemic, has introduced new challenges in retrieving clinically relevant information using conventional search methods. This study proposes a novel, interpretable framework for biomedical information retrieval that integrates Named Entity Recognition (NER), knowledge graph construction, and Graph Neural Networks (GNNs) to support semantic reasoning and entity-level ranking. Unlike prior biomedical retrieval systems that operate at document level or perform link prediction over KGs, our framework introduces a novel task formulation contextual entity-level ranking powered by graph-based semantic reasoning. Leveraging the CORD-19 dataset, the system filters abstracts based on user queries, extracts domain-specific entities using SciSpacy, and constructs a semantic graph that captures co-occurrence relationships among medical concepts. A Graph Convolutional Network (GCN) is then employed to prop- agate relevance signals across the graph, enabling context- aware entity ranking. Experimental evaluations using queries such as ”pneumonia” and ”cough” demonstrate superior performance over traditional IR baselines like TF-IDF and BM25, achieving a Mean Average Precision (MAP) of 0.95 and Precision@3 of 1.00. The results confirm the system’s effectiveness in identifying semantically meaningful biomed- ical entities while offering enhanced transparency through graph-based visualizations. This work contributes a scalable and extensible approach to biomedical search and lays the foundation for intelligent literature exploration in medical research and clinical decision support.

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Author Biographies

Agus Rahmat Fadillah, Nusa Mandiri University

Agus Rahmat Fadillah is a student of the Magister Computer Engineering Program at Nusa Mandiri University, Depok. Currently Participated with some of research regarding information retrieval and deep learning. (Email: 14240002@nusamandiri. ac.id)

Irwansyah Saputra, Universitas Nusa Mandiri

Irwansyah Saputra is a lecturer and researcher specializing in Information Retrieval and Blockchain technologies. He earned his Doctoral degree from IPB University (Institut Pertanian Bogor), Indonesia, where his dissertation focused on blockchain-based key-value storage systems to support dynamic smart contract interactions, particularly within the agromaritime sector. His research interests span across blockchain architecture, semantic search, data mining, and artificial intelligence, with a strong emphasis on real-world applications in agriculture, education, and public services. His academic contributions are widely accessible through Google Scholar and ResearchGate, and he remains actively engaged in developing conceptual and technological innovations that bridge knowledge representation with intelligent systems. (Email: irwansyah.iys@nusamandiri.ac.id)

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