Cognitive Graph for Multi-Hop Reading Comprehension at Scale

Attention Is All You Need

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 · see citing works

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 48,293 · see citing works

Semi-supervised classification with graph convolutional networks

Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works

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 6,912 · see citing works

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 · see citing works

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 · see citing works

The empirical case for two systems of reasoning.

Sloman, S. A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.1.3 · Cited by 3,813 · see citing works

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 2,779 · see citing works

A guided tour to approximate string matching

Navarro, G. (2001) · ACM Computing Surveys · doi:10.1145/375360.375365 · Cited by 2,546 · see citing works

Bidirectional Attention Flow for Machine ComprehensionPreprint

Seo, M., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1611.01603 · Cited by 2,322 · see citing works

In two minds: dual-process accounts of reasoning

Evans, J. S. B. T. (2003) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2003.08.012 · Cited by 2,252 · see citing works

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804 · see citing works

Reading Wikipedia to answer open-domain questions

Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1171 · Cited by 1,546 · see citing works

Adversarial Examples for Evaluating Reading Comprehension Systems

Jia, R., & Liang, P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1215 · Cited by 1,410 · see citing works

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., Zhong, V., Paulus, R., & Socher, R. (2015) · arXiv · doi:10.48550/arxiv.1506.07285 · Cited by 1,020 · see citing works

The TREC-8 Question Answering Track Report

Voorhees, E. M. (1999) · Cited by 843 · see citing works

Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear UnitsPreprint

Hendrycks, D., & Gimpel, K. (2016) · arXiv (Cornell University) · Cited by 753 · see citing works

Gated Self-Matching Networks for Reading Comprehension and Question Answering

Wang, W., Yang, N., Wei, F., Chang, B., & Zhou, M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1018 · Cited by 669 · see citing works

Heuristic and analytic processes in reasoning*

Evans, J. S. B. T. (1984) · British Journal of Psychology · doi:10.1111/j.2044-8295.1984.tb01915.x · Cited by 619 · see citing works

The Web as a Knowledge-Base for Answering Complex Questions

Talmor, A., & Berant, J. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1059 · Cited by 520 · see citing works

The Goldilocks Principle: Reading Children's Books with Explicit Memory RepresentationsPreprint

Hill, F., Bordes, A., Chopra, S., & Weston, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.02301 · Cited by 478 · see citing works

QAngaroo (MedHop + WikiHop) - Constructing Datasets for Multi-hop Reading Comprehension Across Documents

Welbl, J., Stenetorp, P., & Riedel, S. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.1162/tacl_a_00021 · Cited by 459 · see citing works

Simple and Effective Multi-Paragraph Reading Comprehension

Clark, C., & Gardner, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1078 · Cited by 402 · see citing works

R3: Reinforced Ranker-Reader for Open-Domain Question Answering

Wang, S., Yu, M., Guo, X., Wang, Z., Klinger, T., Zhang, W., Chang, S., Tesauro, G., Zhou, B., & Jiang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12053 · Cited by 346 · see citing works

ReasoNet

Shen, Y., Huang, P.-S., Gao, J., & Chen, W. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098177 · Cited by 262 · see citing works

Reinforced Mnemonic Reader for Machine Reading Comprehension

Hu, M., Peng, Y., Huang, Z., Qiu, X., Wei, F., & Zhou, M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/570 · Cited by 199 · see citing works

The structure and performance of an open-domain question answering system

MOLDOVAN, D. I., Harabagiu, S. M., PAŞCA, M. A., Mihalcea, R. F., Gîrju, R., Goodrum, R., & Rus, V. (2000) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1075218.1075289 · Cited by 182 · see citing works

Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering

Wang, W., Yan, M., & Wu, C. (2018) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p18-1158 · Cited by 159 · see citing works

Interaction with Texts: Information Retrieval as Information-Seeking Behavior.

Belkin, N. J. (1993) · Cited by 137 · see citing works

Query-Reduction Networks for Question AnsweringPreprint

Seo, M., Min, S., Farhadi, A., & Hajishirzi, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04582 · Cited by 77 · see citing works

Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories

Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6

Untitled work

(2018) · doi:10.4230/lipics.cp.2025.27

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

Graph neural networks: A review of methods and applications

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880 · see citing works

A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947 · see citing works

Self-supervised Learning: Generative or Contrastive

Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3090866 · Cited by 1,890 · see citing works

TinyBERT: Distilling BERT for natural language understanding

Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L., Wang, F., & Liu, Q. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.372 · Cited by 1,786 · see citing works

Pre-trained models: Past, present and future

Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · see citing works

Towards Knowledge-Based Recommender Dialog System

Chen, Q., Lin, J., Zhang, Y., Ding, M., Cen, Y., Yang, H., & Tang, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1189 · Cited by 281 · see citing works

Hierarchical Graph Network for Multi-hop Question Answering

Fang, Y., Sun, S., Gan, Z., Pillai, R., Wang, S., & Liu, J. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.710 · Cited by 176 · see citing works

Graph Neural Networks: Taxonomy, Advances, and Trends

Zhou, Y. P., Zheng, H., Huang, X., Hao, S., Li, D., & Zhao, J. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3495161 · Cited by 170 · see citing works

MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension

Talmor, A., & Berant, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1485 · Cited by 157 · see citing works

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Asai, A., Hashimoto, K., Hajishirzi, H., Socher, R., & Xiong, C. (2019) · arXiv · doi:10.48550/arxiv.1911.10470 · Cited by 152 · see citing works

AliGraph: A Comprehensive Graph Neural Network Platform

Yang, H. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3340404 · Cited by 196 · see citing works

Retrieving and Reading: A Comprehensive Survey on Open-domain Question AnsweringPreprint

Zhu, F., Lei, W., Wang, C., Zheng, J., Poria, S., & Chua, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00774 · Cited by 237 · see citing works

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

Nie, Y., Wang, S., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1258 · Cited by 132 · see citing works

JAKET: Joint Pre-training of Knowledge Graph and Language Understanding

Yu, D., Zhu, C., Yang, Y., & Zeng, M. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21417 · Cited by 129 · see citing works

Neural, symbolic and neural-symbolic reasoning on knowledge graphs

Zhang, J., Chen, B., Zhang, L., Ke, X., & Ding, H. (2021) · AI Open · doi:10.1016/j.aiopen.2021.03.001 · Cited by 125 · see citing works

TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph

Shi, J., Cao, S., Hou, L., Li, J., & Zhang, H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.341 · Cited by 119 · see citing works

Towards Building The Federatedgpt: Federated Instruction Tuning

Zhang, J., Vahidian, S., Kuo, M., Li, C., Zhang, R., Yu, T., Wang, G., & Chen, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447454 · Cited by 140 · see citing works

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Asai, A. (2025) · TIB Data Manager · doi:10.57702/2jfyctjr · Cited by 109 · see citing works

Contrastive self-supervised learning: review, progress, challenges and future research directions

Kumar, P., Rawat, P., & Chauhan, S. (2022) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-022-00245-6 · Cited by 118 · see citing works

Answering Complex Open-domain Questions Through Iterative Query Generation

Qi, P., Lin, X., Mehr, L., Wang, Z., & Manning, C. D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1261 · Cited by 96 · see citing works

CogLTX: Applying BERT to Long Texts

Ding, M., Zhou, C., Yang, H., & Tang, J. (2020) · neural information processing systems · Cited by 100 · see citing works

A Survey on Machine Reading Comprehension Systems

Baradaran, R., Ghiasi, R., & Amirkhani, H. (2022) · Natural Language Engineering · doi:10.1017/s1351324921000395 · Cited by 101 · see citing works

Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction

Nishida, K., Nishida, K., Nagata, M., Otsuka, A., Saito, I., Asano, H., & Tomita, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1225 · Cited by 97 · see citing works

Open Question Answering over Tables and TextPreprint

Chen, W., Chang, M.-W., Schlinger, E., Wang, W. Y., & Cohen, W. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10439 · Cited by 109 · see citing works

Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations

Lv, X., Gu, Y., Han, X., Hou, L., Li, J., & Liu, Z. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1334 · Cited by 88 · see citing works

Can Graph Neural Networks Count Substructures?Preprint

Chen, Z., Chen, L., Villar, S., & Bruna, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04025 · Cited by 93 · see citing works

Knowledge Guided Text Retrieval and Reading for Open Domain Question AnsweringPreprint

Min, S., Chen, D., Zettlemoyer, L., & Hajishirzi, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03868 · Cited by 82 · see citing works

Deep Transfer Learning for City-scale Cellular Traffic Generation through Urban Knowledge Graph

Zhang, S., Li, T., Hui, S., Guang-yu, L., Liang, Y., Yu, L., Jin, D., & Li, Y. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599801 · Cited by 74 · see citing works

Graph Neural Networks for Natural Language Processing: A SurveyPreprint

Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06090 · Cited by 70 · see citing works

Making Language Models Better Reasoners with Step-Aware Verifier

Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., & Chen, W. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.291 · Cited by 69 · see citing works

Differentiable Reasoning over a Virtual Knowledge BasePreprint

Dhingra, B., Zaheer, M., Balachandran, V., Neubig, G., Salakhutdinov, R., & Cohen, W. W. (2020) · International Conference on Learning Representations · doi:10.48550/arxiv.2002.10640 · Cited by 91 · see citing works

Logic-Guided Data Augmentation and Regularization for Consistent Question Answering

Asai, A., & Hajishirzi, H. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.499 · Cited by 66 · see citing works

Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

Huang, Z., Xu, S., Hu, M., Wang, X., Qiu, J., Fu, Y., Zhao, Y., Peng, Y., & Wang, C. (2020) · IEEE Access · doi:10.1109/access.2020.2988903 · Cited by 63 · see citing works

Towards cognitive intelligence-enabled product design: The evolution, state-of-the-art, and future of AI-enabled product design

Wang, Z., Liang, X., Li, M., Li, S., Liu, J., & Zheng, L. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2024.100759 · Cited by 62 · see citing works

Multi-Document Transformer for Personality Detection

Yang, F., Quan, X., Yang, Y., & Yu, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17673 · Cited by 57 · see citing works

Multi-hop Knowledge Base Question Answering with an Iterative Sequence Matching Model

Lan, Y., Wang, S., & Jiang, J. (2019) · Proceedings · doi:10.1109/icdm.2019.00046 · Cited by 48 · see citing works

Machine Reading Comprehension: The Role of Contextualized Language Models and BeyondPreprint

Zhang, Z., Zhao, H., & Wang, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.06249 · Cited by 54 · see citing works

An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction

Liu, Z., Zhang, D., Jia, W., Lin, X., & Liu, H. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01530-8 · Cited by 44 · see citing works

Towards Accurate and Compact Architectures via Neural Architecture Transformer

Guo, Y. F., Zheng, Y., Tan, M., Chen, Q., Li, Z., Chen, J., Zhao, P., & Huang, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3086914 · Cited by 43 · see citing works

Accelerating Training of Transformer-Based Language Models with Progressive Layer DroppingPreprint

Zhang, M., & He, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13369 · Cited by 47 · see citing works

CogKR: Cognitive Graph for Multi-hop Knowledge Reasoning

Du, Z., Zhou, C., Yao, J., Tu, T., Cheng, L., Yang, H., Zhou, J., & Tang, J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3104310 · Cited by 41 · see citing works

Complex Factoid Question Answering with a Free-Text Knowledge Graph

Zhao, C., Xiong, C., Qian, X., & Boyd-Graber, J. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380197 · Cited by 37 · see citing works

Research on a personalized learning path recommendation system based on cognitive graph with a cognitive graph

Mu, M., & Yuan, M. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2195446 · Cited by 41 · see citing works

Reasoning Like Program Executors

Pi, X., Liu, Q., Chen, B., Ziyadi, M., Lin, Z., Fu, Q., Gao, Y., Lou, J., & Chen, W. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.48 · Cited by 36 · see citing works

Multi-Hop Fact Checking of Political Claims

Ostrowski, W., Arora, A., Atanasova, P., & Augenstein, I. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/536 · Cited by 37 · see citing works

Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering

Hu, Z., Yi‐chong, X., Yu, W., Wang, S., Yang, Z., Zhu, C., Chang, K.-W., & Sun, Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.650 · Cited by 42 · see citing works

Multi-hop Question Answering

Mavi, V., Jangra, A., & Jatowt, A. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000102 · Cited by 79 · see citing works

A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction

Niu, Y., Jiao, F., Zhou, M., Yao, T., Xu, J., & Huang, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.361 · Cited by 33 · see citing works

A Survey on Explainability in Machine Reading ComprehensionPreprint

Thayaparan, M., Valentino, M., & Freitas, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00389 · Cited by 37 · see citing works

Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport

Li, M., Ma, T., Yu, M., Wu, L., Gao, T., Ji, H., & McKeown, K. R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.519 · Cited by 29 · see citing works

Knowledge-based Temporal Fusion Network for Interpretable Online Video Popularity Prediction

Tang, S., Li, Q., Ma, X., Gao, C., Wang, D., Jiang, Y., Ma, Q., Zhang, A., & Chen, H. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3511934 · Cited by 28 · see citing works

Graph Neural Networks for Natural Language Processing: A Survey

Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2023) · doi:10.1561/9781638281436 · Cited by 26 · see citing works

A neural knowledge graph evaluator: Combining structural and semantic evidence of knowledge graphs for predicting supportive knowledge in scientific QA

Qiao, C., & Hu, X. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102309 · Cited by 25 · see citing works

HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions

Li, S., Li, X., Shang, L., Jiang, X., Liu, Q., Sun, C., Ji, Z., & Liu, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17568 · Cited by 23 · see citing works

Adaptive Information Seeking for Open-Domain Question Answering

Zhu, Y., Pang, L., Lan, Y., Shen, H., & Cheng, X. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.293 · Cited by 24 · see citing works

Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based GamesPreprint

Xu, Y., Fang, M., Chen, L., Du, Y., Zhou, J. T., & Zhang, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11655 · Cited by 24 · see citing works

Question Answer System: A State-of-Art Representation of Quantitative and Qualitative Analysis

Zope, B., Mishra, S., Shaw, K., Vora, D. R., Kotecha, K. V., & Bidwe, R. V. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040109 · Cited by 23 · see citing works

Towards Knowledge-Based Recommender Dialog SystemPreprint

Chen, Q., Lin, J., Zhang, Y., Ding, M., Cen, Y., Yang, H., & Tang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.05391 · Cited by 23 · see citing works

Ensemble learning-based approach for improving generalization capability of machine reading comprehension systems

Baradaran, R., & Amirkhani, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.095 · Cited by 24 · see citing works

Cognitive Knowledge Graph Reasoning for One-shot Relational LearningPreprint

Du, Z., Zhou, C., Ding, M., Yang, H., & Tang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05489 · Cited by 22 · see citing works

HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

Gutiérrez, B. J., Shu, Y., Gu, Y., Yasunaga, M., & Su, Y. (2024) · arXiv · doi:10.48550/arxiv.2405.14831 · Cited by 152 · see citing works

AFS Graph: Multidimensional Axiomatic Fuzzy Set Knowledge Graph for Open-Domain Question Answering

Lang, Q., Liu, X., & Jia, W. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3171677 · Cited by 21 · see citing works

The Graph-based Mutual Attentive Network for Automatic Diagnosis

Yuan, Q., Chen, J., Lü, C., & Huang, H. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/469 · Cited by 21 · see citing works

Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning

Guo, X., Yu, M., Gao, Y., Gan, C., Campbell, M. S., & Chang, S. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.624 · Cited by 19 · see citing works

XTQA: Span-Level Explanations for Textbook Question Answering

Ma, J., Chai, Q., Liu, J., Yin, Q., Wang, P., & Zheng, Q. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3294991 · Cited by 20 · see citing works

Breadth First Reasoning Graph for Multi-hop Question Answering

Huang, Y., & Yang, M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.464 · Cited by 20 · see citing works

Reasoning over Hybrid Chain for Table-and-Text Open Domain Question Answering

Zhong, W., Huang, J., Liu, Q., Zhou, M., Wang, J., Yin, J. P., & Duan, N. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/629 · Cited by 20 · see citing works

Answering Any-hop Open-domain Questions with Iterative Document Reranking

Zhang, Y., Nie, P., Ramamurthy, A., & Song, L. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462853 · Cited by 25 · see citing works

Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable

Fu, R., Wang, H., Zhang, X., Zhou, J., & Yan, Y. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.17 · Cited by 20 · see citing works

Representation learning for knowledge fusion and reasoning in Cyber–Physical–Social Systems: Survey and perspectives

Yang, J., Yang, L. T., Wang, H., Gao, Y., Zhao, Y., Xie, X., & Lü, Y. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.09.003 · Cited by 18 · see citing works

Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?

Tang, Y., Ng, H. T., & Tung, A. K. H. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.283 · Cited by 20 · see citing works

Brain and Cognitive Science Inspired Deep Learning: A Comprehensive Survey

Zhang, Z., Ding, X., Liang, X., Zhou, Y., Qin, B., & Liu, T. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3527551 · Cited by 18 · see citing works

Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs

Cai, J., Zhang, Z., Wu, F., & Wang, J. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.19 · Cited by 18 · see citing works

Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot RelationsPreprint

Lv, X., Gu, Y., Han, X., Hou, L., Li, J., & Liu, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11513 · Cited by 18 · see citing works

Cross-Thought for Sentence Encoder Pre-training

Wang, S., Fang, Y., Sun, S., Gan, Z., Cheng, Y., Liu, J., & Jiang, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.30 · Cited by 17 · see citing works

Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge

Ma, K., Cheng, H., Liu, X., Nyberg, E., & Gao, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.392 · Cited by 19 · see citing works

Chain-of-Skills: A Configurable Model for Open-Domain Question Answering

Ma, K., Cheng, H., Zhang, Y., Liu, X., Nyberg, E., & Gao, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.89 · Cited by 22 · see citing works

Asynchronous Multi-grained Graph Network For Interpretable Multi-hop Reading Comprehension

Li, R., Wang, L., Wang, S., & Jiang, Z. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/531 · Cited by 16 · see citing works

Knowledge Base Question Answering through Recursive Hypergraphs

Yadati, N., Dayanidhi, R. S., Vaishnavi, S., Indira, K., & Srinidhi, G. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.35 · Cited by 15 · see citing works

G-NMP: Accelerating Graph Neural Networks with DIMM-based Near-Memory Processing

Tian, T., Wang, X., Zhao, L., Wu, W., Zhang, X., Lu, F., Wang, T., & Jin, X. (2022) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2022.102602 · Cited by 15 · see citing works

Remote sensing knowledge graph construction and its application in typical scenarios

Zhang, Y. C., Wang, F., Li, Y., Ouyang, S., Dong, W. E. I., Liu, X., Deyu, K., Chen, R., & Zhang, B. (2023) · National Remote Sensing Bulletin · doi:10.11834/jrs.20210469 · Cited by 14 · see citing works

ELECTRA-based graph network model for multi-hop question answering

Zhu, P., Yuan, Y., & Chen, L. (2023) · Journal of Intelligent Information Systems · doi:10.1007/s10844-023-00800-5 · Cited by 14 · see citing works

Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension

Wang, W., & Pan, S. J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.343 · Cited by 14 · see citing works

A Cognitive Solver with Autonomously Knowledge Learning for Reasoning Mathematical Answers

Liu, J., Huang, Z., Lin, X., Liu, Q., Ma, J., & Chen, E. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00037 · Cited by 13 · see citing works

Machine Memory Intelligence: Inspired by Human Memory Mechanisms

Zheng, Q., Liu, H., Zhang, X., Yan, C., Cao, X., Gong, T., Liu, Y., Shi, B., Peng, Z., Fan, X., et al. (2025) · Engineering · doi:10.1016/j.eng.2025.01.012 · Cited by 14 · see citing works

Knowledge Reasoning Method for Military Decision Support Knowledge Graph Mixing Rule and Graph Neural Networks Learning together

Nie, K., Zeng, K., & Meng, Q. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9327031 · Cited by 13 · see citing works

Answering Complex Open-domain Questions Through Iterative Query GenerationPreprint

Qi, P., Lin, X., Mehr, L., Wang, Z., & Manning, C. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.07000 · Cited by 20 · see citing works

ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models

Zhang, J., Muhamed, A., Anantharaman, A., Wang, G., Chen, C., Zhong, K., Cui, Q., Xu, Y., Zeng, B., Chilimbi, T. M., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.97 · Cited by 12 · see citing works

Translucent Answer Predictions in Multi-Hop Reading Comprehension

Bhargav, G. P. S., Glass, M., Garg, D. K., 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 13 · see citing works

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 · see citing works

Modeling knowledge proficiency using multi-hierarchical capsule graph neural network

He, Z., Li, W., & Yan, Y. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02765-w · Cited by 12 · see citing works

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 18 · see citing works

Graph neural networks: Historical backgrounds, present revolutions, and conventionalization for the future

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 · see citing works

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 · see citing works

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 10 · see citing works

Prototypical 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 · see citing works

Answer Complex Questions: Path Ranker Is All You Need

Zhang, X., Zhan, K., Hu, E., Fu, C., Luo, L., Jiang, H., Jia, Y., Yu, F., Dou, Z., Cao, Z., et al. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462942 · Cited by 10 · see citing works

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 11 · see citing works

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 8 · see citing works

Knowledge graph reasoning: Mainstream methods, applications and prospects

Liu, H., Yang, S., Shi, G., & Miao, Z. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111625 · Cited by 12 · see citing works

A Content-based Reasoning Method for Multi-hop Question Answering using Graph Neural NetworksPreprint

Ghafouri, A., Naderi, H., & Bidgoli, B. M. (2024) · Research Square · doi:10.21203/rs.3.rs-3909958/v1

Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network

Tang, Z., Shen, Y., Ma, X., Xu, W. H., Yu, J., & Lü, W. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/540 · Cited by 25 · see citing works

StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question AnsweringPreprint

Ni, T., Yuan, X., Li, S., Wu, K., Liu, R. P., Ni, W., & Zhang, W. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2510.02827

A Query-Driven Graph Retrieval Framework with Adaptive Pruning for Multi-Hop Question Answering

Wang, H., Wang, T., Sun, Z., Li, H., Cao, Z., Feng, L., & Wang, D. (2026) · Electronics · doi:10.3390/electronics15061263 · Cited by 3 · see citing works

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Asai, A., Hashimoto, K., Hajishirzi, H., Socher, R., & Xiong, C. (2019) · arXiv · doi:10.48550/arxiv.1911.10470 · Cited by 152 · see citing works