The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
Advances In Deep Learning On Graphs (Gsp'18 Workshop)Preprint
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 7,021
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208
Dual-Processing Accounts of Reasoning, Judgment, and Social Cognition
Evans, J. S. B. T. (2008) · Annual Review of Psychology · doi:10.1146/annurev.psych.59.103006.093629 · Cited by 5,137
The empirical case for two systems of reasoning.
Sloman, S. A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.1.3 · Cited by 3,942
Teaching Machines to Read and ComprehendPreprint
Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,855
A guided tour to approximate string matching
Navarro, G. (2001) · ACM Computing Surveys · doi:10.1145/375360.375365 · Cited by 2,661
Cited by
Sorted by citations
Items 201–224 of 224
Aspect-based Sentiment Classification with Dual Cooperative Graph Attention Networks
Cui, X., & Fang, W. (2022) · International Conference on Machine Learning and Natural Language Processing (MLNLP) · doi:10.1145/3578741.3578769
Using Transformer Towards Cross-Hops Question Answering
Zhou, Y., Zhi, C., & Zheng, K. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-1642-9_28
Retrieving Chinese Questions and Answers Based on Deep-Learning Algorithm
Wang, H., Li, J., & Wang, J. (2023) · Mathematics · doi:10.3390/math11183843 · Cited by 1
何, 奏. (2022) · Operations Research and Fuzziology · doi:10.12677/orf.2022.122016
Multi-hop question answering using sparse graphs
Hemmati, N., & Ghassem-Sani, G. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107128
Cognitive Graph Empowered Textile and Clothing Intelligent Service Platform
Zou, Z., Wang, Z., Liu, L., Feng, C., & Wang, B. (2023) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c60940.2023.00077
A Contextualized Government Service Chatbot for Individuals with limited Information Literacy
Lian, Z., Huang, M., & Wang, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-57850-2_15
Scoring Multi-hop Question Decomposition Using Masked Language Models
Sidhoum, A. H., Mataoui, M., Sebbak, F., Hosni, A. I. E., & Smaïli, K. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3665140
Biomimetic Evolutionary Learning for Intrinsically Interpretable Artificial Intelligence ModelsPreprint
Lei, X., Fan, Y., Hu, Q., Bao, H., & Li, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4846773
Cossentino, M., & Pilato, G. (2024) · Cognitive Systems Research · doi:10.1016/j.cogsys.2024.101257
A Tree-Shaped Fuzzy Clustering Answer Retrieval Model Based on Question Alignment
Lang, Q., Witold Pedrycz, C., Liu, X., & Fang, Y. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3431938
ÇİZGE ÖĞRENMEDE ÇİZGE SİNİR AĞLARI
Gümüş, H. T., & Eyüpoğlu, C. (2024) · İstanbul Ticaret Üniversitesi Teknoloji ve Uygulamalı Bilimler Dergisi · doi:10.56809/icujtas.1442504
Refined Question Reading for Multi-Type Legal Question Answering
Yin, Y., Li, L., Xie, S., Tao, X., & Zhang, J. (2024) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc64747.2024.10780776
A Rule-Based Multidimensional Axiomatic Fuzzy Set Knowledge Graph Question-Answering Model
Du, J. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9671-7_3
Machine‐Reading Comprehension for Bridge Inspection Domain by Fusing Graph Embedding
Xiang, F., Zhu, H., Sun, Y., Zheng, M., & Yan, W. (2025) · Complexity · doi:10.1155/cplx/6691354
KQFV: a knowledge-enhanced method using question answering for fact verification
Bian, Y., & Ma, T. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00956-2
Characterizing Mosaicing Inference Risk: A Preliminary Study
Rollings, N., & Oard, D. W. (2025) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3731120.3744577
Cognitive-driven hierarchical reasoning for event causality identification
Yuan, J., Xu, Y., Zhou, W., Yu, M., Sun, X., Zhang, L., & Luo, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115914
Yang, Y., Wang, T., Pan, M., Zhang, Y., Yue, Y., & Fu, M. (2026) · Science China Information Sciences · doi:10.1007/s11432-025-4852-4
Multi-hop clustering for reasoning chain extraction in multi-hop question answering
JAMEHSHOURANI, M., Fatemi, A., & Nematbakhsh, M. A. (2026) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-026-01203-0
Data and Knowledge Dual-Driven Artificial Intelligence
Wang, H., Wang, M., Liu, L., & Wu, J. (2026) · Springer eBooks · doi:10.1007/978-981-95-5585-7_4
Li, S., Ding, Y., Li, Y., Huang, M., Wu, X., Xu, W., Wang, Y., Xu, J., & Zhou, Z. (2026) · Journal of Visualization · doi:10.1007/s12650-026-01143-3
Financial Sentence Graph Attention Network (FinS-GAT)
Wang, Y., & Liu, Y. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20897-2_21
STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering
Chen, W., Zhao, L., Zheng, Z., Hou, H., & Xu, T. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809703 · Cited by 2
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 ›
Are you an author, or do you see a problem with this page? Request a review
