mlsanityData to 7 Oct 2026

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

arXiv 2201.04672 ↗First posted 12 Jan 2022cs.IR cs.CL cs.LGcited by 7

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Abstract (arXiv)

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 https://github.com/HennyJie/GNN-DocRetrieval.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 12 Jan 2022.

Builds on

21 of 46 references.

References · 21

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9 more
  1. 2019On the Universality of Invariant Networkscited by 151
  2. 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
  3. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
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Cited by year

Citing papers per year: 2022: 1; 2023: 6.

7 papers.

  1. 2023ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrievalcited by 26
  2. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  3. 2023Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Modelscited by 20
  4. 2023Neighborhood-Regularized Self-Training for Learning with Few Labelscited by 14
  5. 2023PTGB: Pre-Train Graph Neural Networks for Brain Network Analysiscited by 10
  6. 2023Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Trainingcited by 8
  7. 2023Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Promptingcited by 5