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The Web as a Knowledge-Base for Answering Complex Questions
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Alon Talmor, Jonathan Berant. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
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“As for other KBQA datasets, e.g., ComplexWebQuestions (Talmor and Berant, 2018), the proportion of implicit relations is also small.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Talmor and Berant, 2018
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Reason-Align-Respond: Aligning LLM Reasoning With Knowledge Graphs for KGQA
Shen, X., Wang, F., Yang, Z., Wang, B., Du, W., Zong, C., & Xia, R. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3665645 · Cited by 7
Linjordet, T., & Balog, K. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531739 · Cited by 7
Multi-hop Question Answering with Knowledge Graph Embedding in a Similar Semantic Space
Li, F., Chen, M., & Dong, R. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892550 · Cited by 5
Discourse-Aware Graph Networks for Textual Logical Reasoning
Huang, Y., Liu, L., Xu, K., Fang, M., Lin, L., & Liang, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3280178 · Cited by 7
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering
Ho, X., Nguyen, A.-K. D., Sugawara, S., & Aizawa, A. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.87 · Cited by 5
Exploring the Reasons for Non-generalizability of KBQA systems
Khosla, S., Dutt, R., Kumar, V. B., & Gangadharaiah, R. (2023) · Workshop on Insights from Negative Results in NLP (Insights) · doi:10.18653/v1/2023.insights-1.11 · Cited by 5
Guo, W., Luo, L., Lai, H., & Yin, J. P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.720 · Cited by 5
Keep the Structure: A Latent Shift-Reduce Parser for Semantic Parsing
Li, Y., Chen, B., Liu, Q., Gao, Y., Lou, J., Zhang, Y., & Zhang, D. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/532 · Cited by 5
Natural Language QA Approaches using Reasoning with External KnowledgePreprint
Baral, C. R., Banerjee, P., Pal, K. K., & Mitra, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03446 · Cited by 5
Shi, J., Cao, S., Pan, L., Xiang, Y., Hou, L., Li, J., Zhang, H., & He, B. (2020) · arXiv (Cornell University) · Cited by 5
Iterative Hierarchical Attention for Answering Complex Questions over Long DocumentsPreprint
Sun, H., Cohen, W. W., & Salakhutdinov, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00200 · Cited by 15
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge GraphsPreprint
Ren, H., Dai, H., Dai, B., Chen, X., Zhou, D., Leskovec, J., & Schuurmans, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.14890 · Cited by 5
MarkQA: A large scale KBQA dataset with numerical reasoning
Huang, X. H., Cheng, S., Bao, Y., Huang, S., & Qu, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.633 · Cited by 4
Flow-guided Direct Preference Optimization for Knowledge Graph Reasoning with Trees
Shen, T., Mao, R., Wang, J., Zhang, X., & Cambria, E. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729980 · Cited by 4
Witschel, H. F., Riesen, K., & Grether, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45439-5_50 · Cited by 4
TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages
Zhao, Z., Chen, L., Cao, R., Xu, H., Chen, X., & Yu, K. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.132 · Cited by 5
Target-driven Attack for Large Language Models
Zhang, C., Jin, M., Shu, D., Wang, T., Liu, D., & Jin, X. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia240685 · Cited by 5
S$$^2$$QL: Retrieval Augmented Zero-Shot Question Answering over Knowledge Graph
Zan, D., Wang, S., Zhang, H., Yan, Y., Wu, W., Guan, B., & Wang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05981-0_18 · Cited by 4
HPE: Answering Complex Questions over Text by Hybrid Question Parsing and Execution
Ye, L., Yavuz, S., Meng, R., Radev, D., Xiong, C., Joty, S., & Zhou, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.293 · Cited by 8
Weakly Supervised Multi-task Learning for Semantic Parsing
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Explanation Regeneration via Multi-Hop ILP Inference over Knowledge Base
Gupta, A., & Srinivasaraghavan, G. (2020) · doi:10.18653/v1/2020.textgraphs-1.13 · Cited by 4
MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs
Siciliani, L., Basile, P., Lops, P., & Semeraro, G. M. (2021) · Semantic Web · doi:10.3233/sw-210440 · Cited by 4
Representing Words in Vector Space and Beyond
Wang, B., Buccio, E. D., & Melucci, M. (2019) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-030-25913-6_5 · Cited by 4
Temporal Reasoning on Implicit Events from Distant SupervisionPreprint
Zhou, B., Richardson, K., Ning, Q., Khot, T., Sabharwal, A., & Roth, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12753 · Cited by 4
VANiLLa : Verbalized Answers in Natural Language at Large ScalePreprint
Biswas, D., Dubey, M., Rony, M. R. A. H., & Lehmann, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.11407 · Cited by 7
Learning to Solve Complex Tasks by Talking to Agents.Preprint
Khot, T., Richardson, K., Khashabi, D., & Sabharwal, A. (2021) · arXiv (Cornell University) · Cited by 4
QUERY2BERT: Combining Knowledge Graph and Language Model for Reasoning on Logical Queries
Phan, T. H. V., & Do, P. (2025) · IEEE Access · doi:10.1109/access.2025.3528097 · Cited by 5
Multi-Path Reasoning for Multi-Hop Question Answering over Knowledge Graph
Lyu, Y., Qin, X., Du, X., Zhao, N., & Qiu, S. (2025) · Chinese Journal of Electronics · doi:10.23919/cje.2023.00.044 · Cited by 3
Metadata
- Authors
- Talmor, A., & Berant, J.
- Affiliations
- Tel Aviv University (IL)
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Published
- 2018
- Pages
- 641-651
- OpenAlex
- W2793065281
- Cited by
- 520
- References
- 0
- Citations by year
- 2026: 75 · 2025: 60 · 2024: 42 · 2023: 74 · 2022: 75 · 2021: 78 · 2020: 59 · 2019: 47 · 2018: 10
- FWCI
- 19.72
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Israel Science Foundation
- Samsung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- complex question answering, question answering, knowledge base construction
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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