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Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Open accessAbstract
We propose a new CogQA framework for multi-hop question answering in web-scale documents. Inspired by the dual process theory in cognitive science, the framework gradually builds a \textit{cognitive graph} in an iterative process by coordinating an implicit extraction module (System 1) and an explicit reasoning module (System 2). While giving accurate answers, our framework further provides explainable reasoning paths. Specifically, our implementation based on BERT and graph neural network efficiently handles millions of documents for multi-hop reasoning questions in the HotpotQA fullwiki dataset, achieving a winning joint $F_1$ score of 34.9 on the leaderboard, compared to 23.6 of the best competitor.
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“In this second category, previous work trains a re-ranking module which can traverse a graph made using Wikipedia hyperlinks [16, 100, 54, 14, 4, 44].”
From HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models (2024) · cites this paper as [16]
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Bhargav, G. P. S., Glass, M., Garg, D., Shevade, S., Dana, S., Khandelwal, D., Subramaniam, L. V., & Gliozzo, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6272 · Cited by 14
Reference Knowledgeable Network for Machine Reading Comprehension
Zhao, Y., Zhang, Z., & Zhao, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3164219 · Cited by 13
Modeling knowledge proficiency using multi-hierarchical capsule graph neural network
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Graph-free Multi-hop Reading Comprehension: A Select-to-Guide StrategyPreprint
Wu, B., Zhang, Z., & Zhao, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11823 · Cited by 24
Maghdid, S. S., Rashid, T. A., & Askar, S. K. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00797-w · Cited by 13
Heterogeneous Mini-Graph Neural Network and Its Application to Fraud Invitation Detection
Zhu, Y.-N., Luo, X., Li, Y., Bu, B., Zhou, K., Zhang, W., & Lu, M. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00098 · Cited by 11
Automated Unsupervised Graph Representation Learning
Hou, Z., Cen, Y., Dong, Y., Zhang, J., & Tang, J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3115017 · Cited by 11
Self-citationPrototypical networks relation classification model based on entity convolution
Feng, J., Wei, Q., & Cui, J. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101432 · Cited by 10
Answer Complex Questions: Path Ranker Is All You Need
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Knowledge-Aided Open-Domain Question AnsweringPreprint
Zhou, M., Shi, Z., Huang, M., & Zhu, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05244 · Cited by 15
Graph-based Multi-hop Reasoning for Long Text GenerationPreprint
Zhao, L., Xu, J., Lin, J., Zhang, Y., Yang, H., & Sun, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.13282 · Cited by 10
Self-citationKnowledge graph reasoning: Mainstream methods, applications and prospects
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Metadata
- Authors
- Ding, M., Zhou, C., Chen, Q., Yang, H., & Tang, J.
- Affiliations
- Alibaba DAMO Academy (China) (CN)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 2694-2703
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2962922117
- Cited by
- 222
- References
- 32
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 19 · 2023: 28 · 2022: 35 · 2021: 60 · 2020: 39 · 2019: 20
- FWCI
- 14.73
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61836013, 61825602)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- question answering, cognitive graphs, dual-process theory, System 1 and System 2, explicit reasoning, HotpotQA, graph neural networks, BERT
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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