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GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings.

Zhiwen Xie1, Runjie Zhu2, Jin Liu1

  • 1School of Computer Science, Wuhan University, Wuhan 430072, China.

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Summary
This summary is machine-generated.

This study introduces GFCNet, a novel approach for knowledge graph embedding (KGE) to address missing information in COVID-19 medical data. GFCNet effectively incorporates attribute and neighbor features, improving relation inference for better pandemic response.

Keywords:
COVID-19Knowledge GraphNatural Language ProcessingText Mining

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Bioinformatics

Background:

  • COVID-19 medical knowledge graphs (KGs) often suffer from incomplete or missing semantic relations.
  • Existing knowledge graph embedding (KGE) models struggle to incorporate crucial features beyond relation triples, such as attribute information, when dealing with complex medical data.

Purpose of the Study:

  • To develop an effective knowledge graph embedding model for the COVID-19 domain that addresses the limitations of existing methods.
  • To enhance the inference of missing relations in COVID-19 medical knowledge graphs by considering both relational and attribute features.

Main Methods:

  • Propose a novel Graph Feature Collection Network (GFCNet) designed to integrate both neighbor and attribute features within KGs.
  • Apply GFCNet to the specific task of knowledge graph embedding for COVID-19 related data, focusing on a COVID-19 drug KG dataset.

Main Results:

  • Experimental results on the COVID-19 drug KG dataset demonstrate the effectiveness and efficiency of the proposed GFCNet model.
  • The model shows promising performance in inferring missing semantic relations, outperforming existing methods that do not fully utilize attribute information.

Conclusions:

  • GFCNet offers a significant improvement for knowledge graph embedding in the medical domain, particularly for complex and incomplete datasets like those related to COVID-19.
  • The findings highlight the importance of incorporating diverse features, including attribute information, for accurate relation inference in medical KGs and suggest future research directions.