Papers with GNN-based methods
An Experimental Analysis on Evaluating Patent Citations (2024.emnlp-main)
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| Challenge: | Graph Neural Networks (GNNs)-based methods can predict patent citations using only patent text. |
| Approach: | They propose to use Graph Neural Networks to predict citations for patents based on their semantic similarities to generate a semantic graph of patents. |
| Outcome: | The proposed methods produce 94% recall for patents with high citations and outperform baselines. |
From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment (2021.emnlp-main)
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| Challenge: | Existing methods for cross-lingual entity alignment rely on lexical matching and probability reasoning, but they inherit poor interpretability and low efficiency from neural networks. |
| Approach: | They propose a simple but effective unsupervised entity alignment method without neural networks that can be used to find the equivalent entities between crosslingual KGs. |
| Outcome: | Extensive experiments show that the proposed method beats advanced supervised methods across all datasets while having high efficiency, interpretability, and stability. |
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification (2021.emnlp-main)
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| Challenge: | Short text classification is a fundamental task in natural language processing. |
| Approach: | They propose a new method called SHINE which is based on graph neural network for short text classification. |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark short text datasets. |
ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction (2023.findings-emnlp)
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| Challenge: | Existing methods for predicting chemical reactions are limited by insufficient training data and inability to utilize textual information. |
| Approach: | They propose a framework that leverages chemical knowledge encoded in language models to assist GNNs, thereby enhancing the accuracy of real-world chemical reaction predictions. |
| Outcome: | The proposed framework improves state-of-the-art GNN-based methods across chemical reaction datasets especially in out-of distribution settings. |
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment (2020.coling-main)
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| Challenge: | Existing methods for cross-lingual entity alignment ignore useful pre-aligned links between two KGs. |
| Approach: | They propose a novel method that jointly learns embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. |
| Outcome: | The proposed method achieves remarkable performance gains on three benchmark cross-lingual entity alignment datasets. |
HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path (2025.emnlp-main)
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| Challenge: | Existing methods for linking knowledge graphs are incomplete and rely on Euclidean embeddings . a hyperbolic GNN framework embeds recursive learning trees in hyperbolical space . |
| Approach: | They propose a hyperbolic GNN framework that embeds recursive learning trees in hyperbolical space and generates query-specific embeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on multiple benchmark datasets. |