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 CORD-19. Results show that instead of the complex
structure-oriented GNNs such as GINs and GATs, our proposed semantics-oriented
graph functions achieve better and more stable performance based on the BM25
retrieved candidates. Our insights in this case study can serve as a guideline
for future work to develop effective GNNs with appropriate semantics-oriented
inductive biases for textual reasoning tasks like document retrieval and
classification. All code for this case study is available at
this https URL.

在大规模跨学科数据集 CORD-19 上进行实证研究，结果表明我们提出的面向语义的图形函数可以通过 BM25 检索的候选项达到更好和更稳定的性能，这可以作为未来开发文档检索和分类等文本推理任务的有效图神经网络的指导。