Structure-aware Retrieval Methods for Web Table Fragments in Question-answering Systems: A Scientific Analytical Review
( Pp. 107-130)
More about authors
Deeb Khaled Ali
postgraduate student
Bauman Moscow State Technical University
Moscow, Russian Federation Gapanyuk Yuriy E. Cand. Sci. (Eng.), Associate Professor; associate professor; Bauman Moscow State Technical University; Moscow, Russian Federation Afanasyev Gennady I. Cand. Sci. (Eng.), Associate Professor; associate professor; Bauman Moscow State Technical University; Moscow, Russian Federation
Bauman Moscow State Technical University
Moscow, Russian Federation Gapanyuk Yuriy E. Cand. Sci. (Eng.), Associate Professor; associate professor; Bauman Moscow State Technical University; Moscow, Russian Federation Afanasyev Gennady I. Cand. Sci. (Eng.), Associate Professor; associate professor; Bauman Moscow State Technical University; Moscow, Russian Federation
Abstract:
Web tables are an important source of structured data for question-answering systems; however, retrieval methods differ substantially in their retrieval units, structure modeling strategies, and mechanisms for matching queries with table representations. The purpose of this article is to systematize methodological directions in structure-aware retrieval of web table fragments and to identify limitations that motivate the use of multivector and late-interaction retrieval schemes. The study is conducted as a scoping review. The search was performed in Scopus, OpenAlex, eLIBRARY/RSCI, and through citation analysis; from 767 identified records, 42 studies were included in the final synthesis after duplicate removal, screening, and full-text assessment. The article proposes a unified formal framework for comparing retrieval methods, develops a taxonomy of approaches, and analyzes retrieval granularity, vector interaction types, relevance aggregation mechanisms, structural signals, and computational limitations. The results show that dense and structure-aware whole-table methods dominate, whereas scalable retrieval of rows, cells, subtables, and multi-table evidence remains insufficiently developed. The article concludes that further research should focus on multivector methods that preserve local table structure while maintaining acceptable computational complexity.
How to Cite:
Khaled Ali Deeb, Gapanyuk Yu.E., and Afanasyev G.I. Structure-aware retrieval methods for web table fragments in question-answering systems: A scientific analytical review. Computational Nanotechnology. 13, 2 (2026), 107–130. DOI: 10.33693/2313-223X-2026-13-2-107-130. EDN: YYPVDQ
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Khattab O., Zaharia M. ColBERT: efficient and effective passage search via contextualized late interaction over BERT. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020. Pp. 39 48. DOI: 10.1145/3397271.3401075.
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Liang H.-P., Chang C.-W., Fan Y.-C. Improving table retrieval with question generation from partial tables. In: Proceedings of the 4th Table Representation Learning Workshop. Vienna: Association for Computational Linguistics, 2025. Pp. 217 228. DOI: 10.18653/v1/2025.trl-1.19.
Liang X., Hu R., Liu Y., Zhu K. Open-domain question an swering over tables with large language models. In: Advanced Intelligent Computing Technology and Applications. Singapore: Springer, 2024. Pp. 347 358. DOI: 10.1007/978-981-97-5615-5 28.
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Luo F., Lan H., Luo H. et al. Decomposition-driven multi-table retrieval and reasoning for numerical question answering. arXiv preprint. 2026. arXiv:2603.07950.
Ni W. Lightweight table retrieval via vector matching for table question answering. In: Proceedings of the 22nd International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). 2025. DOI: 10.1109/ICCWAMTIP68645.2025.11352653.
Pan F., Canim M., Glass M. et al. CLTR: An end-to-end, transformer-based system for cell-level table retrieval and table question answering. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations. 2021. Pp. 202 209. DOI: 10.18653/v1/2021.acl-demo.24.
Pan F., Canim M., Glass M. et al. T-RAG: End-to-end table question answering via retrieval-augmented generation. arXiv preprint. 2022. arXiv:2203.16714.
Pilaluisa J., Tom s D., Navarro-Colorado B., Maz n J.-N. Contextual word embeddings for tabular data search and integration. Neural Computing and Applications. 2023. Vol. 35. No. 13. Pp. 9319 9333. DOI: 10.1007/s00521-022-08066-8.
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Sun C., Cao B., Li T. et al. FGTR: fine-grained multi-table retrieval via hierarchical LLM reasoning. arXiv preprint. 2026. arXiv:2603.12702.
Tchuitcheu W.C., Lu T., Dooms A. Towards platonic representation for table reasoning: A foundation for permutation-invariant retrieval. arXiv preprint. 2026. arXiv:2604.12133.
Trabelsi M., Chen Z., Zhang S. et al. StruBERT: Structure-aware BERT for table search and matching. In: Proceedings of the ACM Web Conference 2022. 2022. 10 p. DOI: 10.1145/3485447.3511972.
Vaswani A., Shazeer N., Parmar N. et al. Attention is all you need. Advances in Neural Information Processing Systems. 2017. Vol. 30. Pp. 5998 6008. DOI: 10.48550/arXiv.1706.03762.
Wang H., Liu A., Wang J. et al. Context retrieval for web tables. In: Proceedings of the International Conference on The Theory of Information Retrieval (ICTIR 2015). 2015. Pp. 251 260. DOI: 10.1145/2808194.2809453.
Wang Z., Dong H., Jia R. et al. TUTA: Tree-based transformers for generally structured table pre-training. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery Data Mining. 2021. Pp. 1780 1790. DOI: 10.1145/3447548.3467434.
Wang Z., Jiang Z., Nyberg E., Neubig G. Table retrieval may not necessitate table-specific model design. In: Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI). 2022. Pp. 36 46. DOI: 10.18653/v1/2022.suki-1.5.
Wu J., Yang L., Li D. et al. MMQA: Evaluating LLMs with multi-table multi-hop complex questions. In: Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025). 2025.
Yao X., Zhang Z., Hu X. et al. COTER: Conditional optimal transport meets table retrieval. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM 2024). 2024. Pp. 911 919. DOI: 10.1145/3616855.3635796.
Yao Y., Lyu X. Multi-agent enhanced retrieval-augmented generation for open-domain table question answering. In: Proceedings of the 18th International Conference on Advanced Computer Theory and Engineering (ICACTE). 2025. Pp. 357 363. DOI: 10.1109/ICACTE66284.2025.11412132.
Zhang S., Balog K. Web table extraction, retrieval, and augmentation: A survey. ACM Transactions on Intelligent Systems and Technology. 2020. Vol. 11. No. 2. Art. 13. Pp. 1 35. DOI: 10.1145/3372117.
Zhang X., Wang D., Dou L. et al. MURRE: Multi-hop table retrieval with removal for open-domain text-to-SQL. In: Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025). 2025. Pp. 5789 5806.
Zhang Y., Chen P.B., Ives Z.G. Searching data lakes for nested and joined data. In: Proceedings of the VLDB Endowment. 2024. Vol. 17. No. 11. Pp. 3346 3359. DOI: 10.14778/3681954.3682005.
Keywords:
web tables, question-answering systems, information retrieval, structured fragments, multivector representation, late interaction.
