Papers › QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, Jure Leskovec
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. In this work, we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph neural networks. We evaluate our model on QA benchmarks in the commonsense (CommonsenseQA, OpenBookQA) and biomedical (MedQA-USMLE) domains. QA-GNN outperforms existing LM and LM+KG models, and exhibits capabilities to perform interpretable and structured reasoning, e.g., correctly handling negation in questions.
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Code
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Code Syntology ran Syntology
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Common Sense Reasoning | CommonsenseQA | QA-GNN | Accuracy | 76.1 | #13 of 38 | Archive leaderboard | report |
| Question Answering | OpenBookQA | AristoRoBERTa + QA-GNN | Accuracy | 82.8 | #20 of 45 | Archive leaderboard | report |
| Question Answering | OpenBookQA | QA-GNN | Accuracy | 82.8 | #21 of 45 | Archive leaderboard | report |
| Question Answering | OpenBookQA | AristoRoBERTa | Accuracy | 77.8 | #25 of 45 | Archive leaderboard | report |
| Riddle Sense | RiddleSense | QAGNN | Accuracy (%) | 67 | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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