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G-SAP: Graph-based Structure-Aware Prompt Learning over Heterogeneous Knowledge for Commonsense Reasoning
Open accessAbstract
Commonsense question answering has demonstrated considerable potential across various applications like assistants and social robots.Although fully fine-tuned Pre-trained Language Model(PLM) has achieved remarkable performance in commonsense reasoning, their tendency to excessively prioritize textual information hampers the precise transfer of structural knowledge and undermines interpretability.Some studies have explored combining Language Models (LM) with Knowledge Graphs (KGs) by coarsely fusing the two modalities to perform Graph Neural Network (GNN)-based reasoning that lacks a profound interaction between heterogeneous modalities.In this paper, we propose a novel Graph-based Structure-Aware Prompt Learning Model for commonsense reasoning, named G-SAP, aiming to maintain a balance between heterogeneous knowledge and enhance the cross-modal interaction within the LM+GNNs model.In particular, an evidence graph is constructed by integrating multiple knowledge sources, i.e.ConceptNet, Wikipedia, and Cambridge Dictionary to boost the performance.Afterward, a structure-aware frozen PLM is employed to fully incorporate the structured and textual information from the evidence graph, where the generation of prompts is driven
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“Guilford's Structure of Intellect model provides psychological foundations for categorizing cognitive operations such as pattern recognition, memory retrieval, and evaluation, enhancing reasoning clarity, coherence, and adaptability [562, 195].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [195]
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Sorted by citations
A Survey of Context Engineering for Large Language Models
Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.13334 · Cited by 281
Metadata
- Authors
- Dai, R., Tan, Y., Mo, L., Liang, S., Huo, G., Luo, J., & Cheng, Y.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Venue
- Proceedings of the 2024 International Conference on Multimedia Retrieval
- Publisher
- ACM
- Published
- 2024-05-30
- Pages
- 1051-1060
- OpenAlex
- W4399418463
- Cited by
- 1
- References
- 4
- Citations by year
- 2025: 1
- FWCI
- 0.29
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Multimedia Retrieval (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- commonsense reasoning, commonsense question answering, prompt learning, knowledge graphs, graph neural networks, knowledge heterogeneity, cross-modal interaction, pre-trained language models, evidence graph, ConceptNet, Wikipedia, interpretability
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- Dai, R., Tan, Y., Mo, L., Liang, S., Huo, G., Luo, J., & Cheng, Y. (2024). G-SAP: Graph-based Structure-Aware Prompt Learning over Heterogeneous Knowledge for Commonsense Reasoning. Proceedings of the 2024 International Conference on Multimedia Retrieval. https://doi.org/10.1145/3652583.3658040
