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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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Li, J., Peng, Z., Zhu, X., & Lu, K. (2022) · International Conference on Ubiquitous Computing and Communications (IUCC) · doi:10.1109/iucc-cit-dsci-smartcns57392.2022.00067 · Cited by 1
A Method for Complex Question-Answering over Knowledge Graph
Yang, L., Guo, H., Dai, Y., & Chen, W. (2023) · Applied Sciences · doi:10.3390/app13085055 · Cited by 2
Multi-view consistency for multi-hop knowledge base question answering
Wang, X. (2023) · International Conference on Information Science, Electrical and Automation Engineering (ISEAE) · doi:10.1117/12.2689748 · Cited by 1
Efficient Question Answering Based on Language Models and Knowledge Graphs
Li, F., Huang, H., & Dong, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44216-2_28 · Cited by 1
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A Relation Embedding Assistance Networks for Multi-hop Question Answering
Jiao, S., Zhu, Z., Qi, J., Xu, F., Pei, H., Wang, W.-L., Song, Z., & Liu, P. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3635114 · Cited by 1
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A Modular Dual Learning for Improving Question Answering and Generation Over Knowledge Graphs
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Inferring Logical Clauses for Answering Complex Multi-hop Open Domain Questions
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Using knowledge bases for question answering
Lan, Y. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University)
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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