How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation

@inproceedings{Cui2022HowCG,
  title={How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation},
  author={Hejie Cui and Jiaying Lu and Yao Ge and Carl Yang},
  booktitle={ECIR},
  year={2022}
}
Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks. With unstructured texts represented as concept maps, GNNs can be exploited for tasks like document retrieval. Intrigued by how can GNNs help document retrieval, we conduct an empirical study on a large-scale multi-discipline dataset CORD19. Results show that instead of the complex structure-oriented GNNs such as GINs and GATs, our… 

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