The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The Web as a Knowledge-Base for Answering Complex Questions
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 41
- Y. Artzi and L. Zettlemoyer. 2013. Weakly supervised learning of semantic parsers for mapping instruc- tions to actions. Transactions of the Association for Computational Linguistics (TACL) 1:49-62.
- J. Bao, N. Duan, Z. Yan, M. Zhou, and T. Zhao. 2016. Constraint-based question answering with knowl- edge graph. In International Conference on Com- putational Linguistics (COLING).
- J. Berant, A. Chou, R. Frostig, and P. Liang. 2013. Se- mantic parsing on Freebase from question-answer pairs. In Empirical Methods in Natural Language Processing (EMNLP).
- J. Berant and P. Liang. 2015. Imitation learning of agenda-based semantic parsers. Transactions of the Association for Computational Linguistics (TACL) 3:545-558.
- K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In International Conference on Management of Data (SIGMOD). pages 1247-1250.
- C. Buck, J. Bulian, M. Ciaramita, A. Gesmundo, N. Houlsby, W. Gajewski, and W. Wang. 2017. Ask the right questions: Active question reformu- lation with reinforcement learning. arXiv preprint arXiv:1705.07830 . · arXiv
- D. Chen, J. Bolton, and C. D. Manning. 2016. A thor- ough examination of the CNN / Daily Mail reading comprehension task. In Association for Computa- tional Linguistics (ACL).
- D. Chen, A. Fisch, J. Weston, and A. Bordes. 2017. Reading Wikipedia to answer open-domain ques- tions. arXiv preprint arXiv:1704.00051 . · arXiv
- K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Ben- gio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 . · arXiv
- C. Clark and M. Gardner. 2017. Simple and effec- tive multi-paragraph reading comprehension. arXiv preprint arXiv:1710.10723 . · arXiv
- J. Duchi, E. Hazan, and Y. Singer. 2010. Adaptive sub- gradient methods for online learning and stochastic optimization. In Conference on Learning Theory (COLT).
- M. Dunn, , L. Sagun, M. Higgins, U. Guney, V. Cirik, and K. Cho. 2017. SearchQA: A new Q&A dataset augmented with context from a search engine. arXiv .
- K. Guu, P. Pasupat, E. Z. Liu, and P. Liang. 2017. From language to programs: Bridging reinforce- ment learning and maximum marginal likelihood. In Association for Computational Linguistics (ACL).
- K. M. Hermann, T. Koisk, E. Grefenstette, L. Espe- holt, W. Kay, M. Suleyman, and P. Blunsom. 2015. Teaching machines to read and comprehend. In Ad- vances in Neural Information Processing Systems (NIPS).
- D. Hewlett, A. Lacoste, L. Jones, I. Polosukhin, A. Fandrianto, J. Han, M. Kelcey, and D. Berthelot. 2016. Wikireading: A novel large-scale language understanding task over Wikipedia. In Association for Computational Linguistics (ACL).
- F. Hill, A. Bordes, S. Chopra, and J. Weston. 2015. The goldilocks principle: Reading children's books with explicit memory representations. In International Conference on Learning Representations (ICLR).
- M. Iyyer, W. Yih, and M. Chang. 2016. Answering complicated question intents expressed in decom- posed question sequences. CoRR 0.
- R. Jia and P. Liang. 2017. Adversarial examples for evaluating reading comprehension systems. In Em- pirical Methods in Natural Language Processing (EMNLP).
- M. Joshi, E. Choi, D. Weld, and L. Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised chal- lenge dataset for reading comprehension. In Associ- ation for Computational Linguistics (ACL).
- J. Krishnamurthy and T. Mitchell. 2012. Weakly supervised training of semantic parsers. In Em- pirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL). pages 754-765.
- T. Kwiatkowski, E. Choi, Y. Artzi, and L. Zettlemoyer. 2013. Scaling semantic parsers with on-the-fly on- tology matching. In Empirical Methods in Natural Language Processing (EMNLP).
- G. Lai, Q. Xie, H. Liu, Y. Yang, and E. Hovy. 2017. Race: Large-scale reading comprehen- sion dataset from examinations. arXiv preprint arXiv:1704.04683 . · arXiv
- K. lee, M. Lewis, and L. Zettlemoyer. 2016. Global neural CCG parsing with optimality guarantees. In Empirical Methods in Natural Language Processing (EMNLP).
- P. Liang. 2013. Lambda dependency-based composi- tional semantics. arXiv preprint arXiv:1309.4408 . · arXiv
- P. Liang, M. I. Jordan, and D. Klein. 2011. Learn- ing dependency-based compositional semantics. In Association for Computational Linguistics (ACL). pages 590-599.
- T. Nguyen, M. Rosenberg, X. Song, J. Gao, S. Tiwary, R. Majumder, and L. Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In Workshop on Cognitive Computing at NIPS.
- R. Nogueira and K. Cho. 2016. End-to-end goal-driven web navigation. In Advances in Neural Information Processing Systems (NIPS).
- T. Onishi, H. Wang, M. Bansal, K. Gimpel, and D. McAllester. 2016. Who did what: A large-scale person-centered cloze dataset. In Empirical Meth- ods in Natural Language Processing (EMNLP).
- P. Pasupat and P. Liang. 2015. Compositional semantic parsing on semi-structured tables. In Association for Computational Linguistics (ACL).
- J. Pennington, R. Socher, and C. D. Manning. 2014. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP).
- M. Steedman. 2000. The Syntactic Process. MIT Press.
- I. Sutskever, O. Vinyals, and Q. V. Le. 2014. Sequence to sequence learning with neural networks. In Ad- vances in Neural Information Processing Systems (NIPS). pages 3104-3112.
- O. Vinyals, M. Fortunato, and N. Jaitly. 2015. Pointer networks. In Advances in Neural Information Pro- cessing Systems (NIPS). pages 2674-2682.
- W. Wang, N. Yang, F. Wei, B. Chang, and M. Zhou. 2017. Gated self-matching networks for reading comprehension and question answering. In Associ- ation for Computational Linguistics (ACL).
- Y. Wang, J. Berant, and P. Liang. 2015. Building a semantic parser overnight. In Association for Com- putational Linguistics (ACL).
- Y. Watanabe, B. Dhingra, and R. Salakhutdinov. 2017. Question answering from unstructured text by retrieval and comprehension. arXiv preprint arXiv:1703.08885 . · arXiv
- J. Welbl, P. Stenetorp, and S. Riedel. 2017. Construct- ing datasets for multi-hop reading comprehension across documents. arXiv preprint arXiv:1710.06481 . · arXiv
- W. Yih, M. Richardson, C. Meek, M. Chang, and J. Suh. 2016. The value of semantic parse labeling for knowledge base question answering. In Associ- ation for Computational Linguistics (ACL).
- M. Zelle and R. J. Mooney. 1996. Learning to parse database queries using inductive logic program- ming. In Association for the Advancement of Arti- ficial Intelligence (AAAI). pages 1050-1055.
- L. S. Zettlemoyer and M. Collins. 2005. Learning to map sentences to logical form: Structured classifica- tion with probabilistic categorial grammars. In Un- certainty in Artificial Intelligence (UAI). pages 658- 666.
- V. Zhong, C. Xiong, and R. Socher. 2017. Seq2sql: Generating structured queries from natural lan- guage using reinforcement learning. arXiv preprint arXiv:1709.00103 . · arXiv
Cited by
Sorted by citations
Items 401–500 of 529
Answering Complex Questions Using Curated and Extracted Knowledge Bases
Bhutani, N. (2019) · Deep Blue (University of Michigan)
Extracting the Unknown from Long Math ProblemsPreprint
Nakashole, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12048
Understanding Natural Language with Commonsense Knowledge Representation, Reasoning, and Simulation
Bosselut, A. (2020) · ResearchWorks at the University of Washington (University of Washington)
Accessing Diverse Web Knowledge with Natural Language Interface
chen, W. (2021) · eScholarship (California Digital Library)
Case-based Reasoning for Natural Language Queries over Knowledge BasesPreprint
Das, R., Zaheer, M., Thai, D., Godbole, A., Perez, E., Lee, J. Y., Tan, L., Polymenakos, L., & McCallum, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08762 · Cited by 34
Extract, Integrate, Compete: Towards Verification Style Reading ComprehensionPreprint
Zhang, C., Lai, Y., Feng, Y., & Zhao, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05149
Decomposing Complex Questions Makes Multi-Hop QA Easier and More InterpretablePreprint
Fu, R., Wang, H., Zhang, X., Zhou, J., & Yan, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.13472 · Cited by 6
SPARQLing Database Queries from Intermediate Question DecompositionsPreprint
Saparina, I., & Osokin, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06162
Asadifar, S., Kahani, M., & Shekarpour, S. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.846
Query and Neighbor-Aware Reasoning Based Multi-hop Question Answering over Knowledge Graph
Ma, B., Chen, X., & Xiong, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10983-6_11
Linjordet, T., Balog, K., & Setty, V. (2022) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3539813.3545146
A Question Embedding-based Method to Enrich Features for Knowledge Base Question Answering
Wang, X., Lin, M., & Lü, Q. (2022) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas48785.2022.9937823
Towards Ontology-Based End-to-End Domain-Oriented KBQA System
Zakharova, A., Sorokina, D., Alexandrov, D., & Butakov, N. A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-23210-7_4
Schema Item Matters in Knowledge Base Question Answering
Wen, Z., Si, Q., Lin, Z., Liu, H., Fu, P., & Wang, W. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191152
The Fourth Workshop on Insights from Negative Results in NLP
Tafreshi, S., Akula, A., Sedoc, J., Валентинович, Д. А., Rogers, A., & Rumshisky, A. (2023) · doi:10.18653/v1/2023.insights-1
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
When Do Decompositions Help for Machine Reading?
Wei, K., Lawrie, D., Durme, B. V., Chen, Y., & Weller, O. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.219 · Cited by 2
BEKBQA: Improving Knowledge Base Question Answering with a Bidirectional Entity Scoring Model
Li, J., He, L. J., Ma, H., Wang, S., Yi, S., & Hu, W. (2023) · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · doi:10.1109/prml59573.2023.10348352
Cação, F. N. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.3.2023.tde-10012024-092904
Zhen, C., Niu, J., Mo, S., & Chen, J. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads60453.2023.00166
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
A Review on Question Answering System over Knowledge GraphPreprint
Choudhury, B., & Deka, V. (2024) · doi:10.22541/au.171618466.63003321/v1
Toward Space-Efficient Semantic Querying with Graph Databases
Kulkarni, G., Shahi, S., & Sirmokadam, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3242-5_10
Chen, Y., Li, Y., & Wen, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4906050
Complex Knowledge Base Question Answering via Structure and Content Dual-Driven Method
Zhang, J., Zhang, L., Zhang, J., Xin, Y., & Zheng, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-7235-3_16
Knowledge-injected Stepwise Reasoning on Complex KBQA
Hu, X., Yang, J., & Gang, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650658
Learning Choice Nuance for Multiple-Choice Commonsense Question Answering
Yang, D., Deng, W., Wang, Z., Wang, K., Zhuang, Z., & Li, H. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651121
Enhancing the Completeness of Rationales for Multi-Step Question Answering
Xue, S., Huang, Z., Lin, X., Liu, J., Qin, L., Su, T., Liu, H., & Liu, Q. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679660
KG-CQAM: knowledge graph and mind-mapping-based complex question answering for large language models
Lai, X., Tang, L., Zhu, X., Xiao, L., Chen, Z., & Yang, J. (2024) · International Conference on Control, Robotics and Intelligent System (CCRIS) · doi:10.1117/12.3050113
Schema Retrieval for Korean Geographic Knowledge Base Question Answering Using Few-Shot Prompting
Lee, S. E., & Yu, K. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13120453
Wang, Z., Huo, H., Xu, R., Yang, S., & Fang, T. (2024) · International Conference on Machine Learning and Natural Language Processing (MLNLP) · doi:10.1109/mlnlp63328.2024.10800230
Jeon, M., Yeom, K., Park, J., Kim, M., & Lee, K.-H. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116408
A LLM Enhanced Two-Stage Approach for Multi-hop Knowledge Graph Reasoning
Han, D., Zhao, Z., & Liu, C. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-2376-1_23
BRDIA: Bidirectional Reasoning with Dynamic Instruction Adjustment for Multi-hop KGQA
Gong, C., & Wei, Z. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888191
HR-SKGs: Hyper-Relational Semantic Knowledge Graphs for Multi-hop Reading Comprehension
Zhang, Y., Cheng, B. H., & Chen, Y.-L. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889591
SH-CoDE: Scholarly Hybrid Complex Question Decomposition and Execution
Taffa, T. A., & Usbeck, R. (2025) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc64641.2025.00024 · Cited by 1
GOSP: A Granularity-Optimized SPARQL Generation Framework for Knowledge Base Question Answering
Gao, B., Zhang, Y., Xiang, G., & Zhou, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8180-8_1
CoMuS-KG: A Collaborative Framework of Multimodal Unstructured Data and Knowledge Graph
Hu, S., Wang, X., Ji, X., Guo, X., Wang, L., & Shen, J. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033342
TiconvQA: A Tibetan Conversational Dataset for Text Comprehension
Cairang, P., Cairen, D., & Sun, Y. (2025) · Data Intelligence · doi:10.3724/2096-7004.di.2025.0042
Complex Question Decomposition Based on Causal Reinforcement Learning
Li, D., Lu, Y., Wu, J., Zhou, W., & Zeng, G. (2025) · Symmetry · doi:10.3390/sym17071022
LLM-based Search Assistant with Holistically Guided MCTS for Intricate Information Seeking
Ren, R., Wang, Y., Li, J., Jiang, J., Zhao, W. X., Wang, W., & Chua, T. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730025 · Cited by 6
CCSK: Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models
Lu, J., & Lv, M. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_3
Optimizing Knowledge Graph Reasoning via Global Relation Pruning with LLMs
Xu, T., & Zhu, W. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0020-8_30
Kim, W. K., & Kim, W. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103784
Machines flunking an exam: Evaluating large language models on course-related open questionsPreprint
Huang, J., Wei, Y., Zhang, L., Lai, R., Lai, F., & Zheng, Y. (2025) · Research Square · doi:10.21203/rs.3.rs-6679662/v1
Adaptive complex knowledge base question answering with program decomposition chain
Wang, D., Zhou, S., Liang, K., Liu, G., Qian, C., Ren, H., & Huang, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131736
The Graph Language: How Knowledge Graphs Speak to Large Language Models
Pirrò, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-09527-5_33
Exemplar-Guided Planning: Enhanced LLM Agent for KGQA
Xu, J., Ma, S., Song, X., Jiang, R., Tu, H., & Zhou, B. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4088-4_14
ESSI-KG: Enhancing Structural Semantic Integration in KG-to-Text Pretraining Models
Tie, X., Liu, R., Luo, X., Xie, S., & Wang, X. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229155
IKG-Agent: Intent-driven Knowledge Graph Agent for Adaptive Workflow Reasoning
Liang, Y., Tao, S., Teng, H., Yao, H., Wang, X., Guo, X., Wang, L., & Ji, X. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228190 · Cited by 1
SCIGENT: Multi-agent for End-to-end Question Answering on Scientific Knowledge Graph
Nguyen, D.-H., Le, N.-K., Hasegawa, S., & Nguyen, M. L. (2025) · International Conference on Knowledge and Systems Engineering · doi:10.1109/kse68178.2025.11309536 · Cited by 1
Contrastive Learning-Enhanced Chain-of-Thought Optimization for Complex Table Question Answering
Yin, J., Liu, P., Mao, X., Zou, Z., Zhang, L., Wang, F., Yan, J., Sun, X., & Xu, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3649651
Memory-Augmented Agent Training for Business Document Understanding
Liu, J., Zeng, Y., Højmark‐Bertelsen, M., Gadeberg, M. N., Wang, H., & Wu, Q. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-11477-8_5 · Cited by 3
Huang, P., Li, B., Wu, W., Wang, H., Xu, M., Chen, C., Liang, L., & Wang, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-3830-0_20
SPPBench: A Comprehensive Sports Psychology Performance Benchmark for LLMs
Guo, X., & Xu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4445-5_8 · Cited by 1
Efficient GraphRAG via Community Embedding for Global and Reasoning-Intensive Question Answering *
Yang, Q., Chen, K., Liu, X., Zhang, T., & Huang, D. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342988
Knowledge Graph-Enhanced Large Language Model Reasoning with Prompt Engineering
Rui, Z., Fu, S., Xiang, H., Wu, S., Chen, S., & Xu, X. (2025) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd69312.2025.00043
S2Q: Teaching Language Models New Facts Through Knowledge Graph Instruction Synthesis
Zhao, Z., Cao, X., & Zhang, W. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6196-4_17
Advancing faithful KBQA services via prompt-driven knowledge graph-enhanced LLMs in cloud
Rui, Z., Chen, S., Fu, S., Sun, W., & Xue, S. (2026) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-026-00855-z
Chen, W., Zhan, J., & Ma, W. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402308
Cao, B., Lu, G., & Shen, L. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401568
Efficient LLM-Based Subgraph Retrieval for Multi-Hop Knowledge Base Question Answering
Yuan, D., Zhou, S. K., Chen, X., Chen, H., Liang, K., & Huang, J. X. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3679080
SSFGraphRAG: Semantic-Structure Fusion Graph Retrieval-Augmented Generation
Liu, B., Wang, M.-H., Wang, J., Xu, B., Gan, J., & Han, X. (2025) · International Conference on Vision, Image and Signal Processing (ICVISP) · doi:10.1109/icvisp68610.2025.11451238
Wang, Z., & Chen, W. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_2
Generating and answering questions across text and knowledge graphs
Han, K. (2024) · theses.fr (ABES) · doi:10.70675/605774e1z4ab3z4bc8z9628ze56d226162f9
Répondre aux questions visuelles à propos d'entités nommées
Lerner, P. J. (2023) · theses.fr (ABES) · doi:10.70675/78c96766zfc95z4da8zb67aze67555da6c75
Um, K., Yeom, K., Yang, H., Choi, M., Yang, H., & Lee, K.-H. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792730 · Cited by 1
Evaluating GraphRAG for Traceable and Interpretable Question Answering
Ahmad, A., Bracken, I. D., & Mersereau, L. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11468256
Accelerating KBQA via Logical-Question Bidirectional Reranking
Wang, X., Shi, S., Wang, L., Zhao, S., Cheng, B. H., & Dong, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463725
Zhai, Y., Lin, T., Song, X., Liu, H., Gao, Y., Chen, K., Feng, D., Ding, B., & Wang, H. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463917
Zhao, M., Tianxi, H., Duan, G., & Wang, S. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463795
HREI: Hybrid Long-Short Retrieval and Efficient Inference for Knowledge Base Question Answering
Liu, S., Su, X., Li, J., Duo, Z., & Gao, G. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464217
Enhancing Knowledge base Question Answering with Reinforced Hop-Wise Logical form Generation
Tang, J., Cao, J., Gao, J., & Bu, R. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463250
ActiveParam: Selective Parameterization for Efficient and Robust Retrieval-Augmented Generation
Li, S., Liu, X., Peng, P., & Liu, S. H. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463531
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
SQA: SPARQL Query Annotating with Question-Answer Pairs
Bao, Y., Zhou, W., Wu, X., Hu, W., Xu, D., 钱铭佳, & Qu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-25156-5_3
CR-TKGQA: A Temporal Knowledge Graph Question Answering Dataset Involving Complex Reasoning
Bao, Y., Xu, D., Longfei, E., Shen, J., Cheng, G., & Qu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-25159-6_9
HGCQA: A Benchmark for Complex Question Answering in Chinese History and Geography
Zhen, M., Zhang, X., Chen, B., & Zhao, X. (2026) · International Conference on AI in Education Technology and Applications (AIETA) · doi:10.1109/aieta69357.2026.11508795
Consensus-on-Graph: Plan-Driven Exploration and Consensus Decision-Making on Knowledge Graphs
Hu, C., Zhan, B., Yuan, L., Fan, W., & Yan, S. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-0369-7_32
AdaGV: An Adaptive Generation-Validation Framework for Food Flavor QA Construction
Liu, L., Jian, C., Hao, Z., Li, H., & Zhang, B. (2025) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait67930.2025.11522006
Chen, Y., Li, S., Liu, B., Huo, S., Xu, K., Zhang, Z., & Xie, Y. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3696013
Hop-wise Planning with Iterative Explainable Self-Correction for Knowledge Base Question Answering
Huang, D., Gao, J., Luo, X., Wang, X., Hao, W. U., & Yu, H. (2026) · ACM Transactions on Information Systems · doi:10.1145/3819823
Extracting Product Carbon Footprint in PDF Documents using Question Answering Framework
Zhao, K., Balaji, B., & Lee, S. (2026) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3744255.3798128
BrickQA: Bridging the Semantic Gap in Building Operations with Dynamic Graph Exploration
Ko, Y.-D., Jung, H. E., Pritoni, M., & Jain, R. K. (2026) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3744256.3812570
Li, J., Chen, L., & Chen, Z. (2026) · International Conference on Sensor-Cloud and Edge Computing System (SCECS) · doi:10.1109/scecs69812.2026.11565843
VIKA: Vectorized Indispensable Knowledge-Subgraph Augmentation for Large Language Models
Xie, B., Wang, S., Ding, L., Chen, J., & Xiang, Y. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-8525-0_7
Zhao, K., Hou, J., Zhang, L. H., Wu, Z., Li, Q., & Wei, S. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582040
Tang, R., & Wu, F. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582239
GT-RAG: Knowledge Graph-based Retrieval-Augmented Generation for Multi-hop Question Answering
Lu, X., Luo, S., Zhao, Q., & Rao, W. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582056
SPARQ: Graph-Structured Queries Drive GNN Retrieval for Knowledge Graph Question Answering
Wang, M. Y., Wang, X., Liu, R., Li, Y. Z., & Luo, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2494-4_9
KG-ART: Dual-Track Adversarial Reasoning for Knowledge Graph Question Answering
Guo, Y., Song, J., Cao, X., Yin, F., & Leng, H. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2856-0_2
DeLFKBQA: Decoupled Logical Form Generation for Knowledge Base Question Answering
Wang, X., Shi, S., Wang, L., Zhao, S., Cheng, B. H., & Dong, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2852-2_2
MMoERAG: A Multi-Source Mixture-Of-Experts Retrieval-Augmented Generation Framework
Jie, Y., & Wu, Z. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-3560-5_13
GCA-KBQA: A Step-Wise Logical Form Generation Approach for KBQA with Knowledge-Assisted Calibration
Bu, R., Cao, J., Gao, J., Tang, J., Qian, S., & Cai, H. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809660
RelGraphQA: Relation-Driven KGQA with Schema-Indexed Grounding
Wan, Y., & Wen, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2759-4_39
Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method
Zhao, T., Chen, J., Zhang, S., Zhu, H., Lin, Q., & Liu, J. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809650
Exploration-and-Thinking: Agentic Reasoning over Knowledge Graphs via an LLM-RL Synergized Framework
Xia, Y., Zhou, G., Chen, J., Chen, X., Fan, Q., & Li, S. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809649
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 ›
Are you an author, or do you see a problem with this page? Request a review