Question answering by reasoning across documents with graph convolutional networks
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Nicola De Cao, Wilker Aziz, Ivan Titov. 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). 2019.
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Graph Neural Networks enhance RAG systems by addressing limitations in handling structured knowledge, with GNNs excelling at capturing entity associations and improving knowledge consistency.
“Graph Neural Networks enhance RAG systems by addressing limitations in handling structured knowledge, with GNNs excelling at capturing entity associations and improving knowledge consistency [232, 116].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [116]
Section 2.2 describes a graph linking entity mentions across documents, and the Graph edges ablation reports that modeling those relations improves multi-document question answering. The paper does not study RAG systems or establish that GNNs improve knowledge consistency in them. (LLM, conf=0.87)
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Cites
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- Authors
- De Cao, N., Aziz, W., & Titov, I.
- Affiliations
- University of Amsterdam (NL)
- Venue
- 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)
- Published
- 2019
- Pages
- 2306-2317
- OpenAlex
- W2889283903
- Cited by
- 54
- References
- 6
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 8 · 2022: 7 · 2021: 16 · 2020: 8 · 2019: 7 · 2018: 1
- FWCI
- 3.63
- 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)
- UvA-DARE (University of Amsterdam) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (277-89-002, 639.022.518)
- H2020 European Research Council (678254)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- question answering, graph convolutional networks, knowledge graphs, multi-document question answering
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