A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
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Tahira Naseem, Srinivas Ravishankar, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Young-Suk Lee, Pavan Kapanipathi, Salim Roukos, Alfio Gliozzo, Alexander Gray. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2021.
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Existing relation linking methods mainly rely on textual similarities between natural language and knowledge graph to build relation links.
“Existing methods mainly rely on the textual similarities between NL and KG to build relation links.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Naseem et al., 2021
The Introduction says relation linking is addressed primarily using question text, and Related Work says most earlier methods rely on question text or its dependency parse. Neither passage establishes that these methods mainly rely on textual similarity between natural language and knowledge-graph labels; Related Work instead describes a range of rules and NLP tools. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSimReL reports its F1-score improvement on LC-QuAD as 2.4%.
“As a reference, SimReL reports its F1-score improvement on LC-QuAD as 2.4% (Naseem et al., 2021).”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Naseem et al., 2021
The Results and Discussion section reports that incorporating AMR into relation linking improved the NSQA system’s F1 on the LC-QuAD 1.0 test set by 2.4, reaching 44.5. It does not describe this as a 2.4% relative improvement, and the reported gain is for the integrated KBQA system rather than SemReL’s standalone relation-linking F1. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSimReL uses AMR for question representation and employs a Siamese network with BERT to link relations in an end-to-end manner.
“SimReL (Naseem et al., 2021) also uses AMR for question representation, and employs a Siamese network with BERT to link relations in an end-to-end manner.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Naseem et al., 2021
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Cites
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Research works printed but not linked: 21
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Cited by
Sorted by citations
Physics of Language Models: Part 3.1, Knowledge Storage and Extraction
Allen-Zhu, Z., & Li, Y. (2023) · arXiv · doi:10.48550/arxiv.2309.14316 · Cited by 14
Implicit Relation Linking for Question Answering over Knowledge Graph
Zhao, Y., Huang, J., Hu, W., Chen, Q., Qiu, X., Huo, C., & Ren, W. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.312 · Cited by 5 · see citing works
- Authors
- Naseem, T., Ravishankar, S., Mihindukulasooriya, N., Abdelaziz, I., Lee, Y.-S., Kapanipathi, P., Roukos, S., Gliozzo, A., & Gray, A.
- Affiliations
- IBM (United States) (US)
- ORCID
- Venue
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Published
- 2021
- Pages
- 256-262
- OpenAlex
- W3177355445
- Cited by
- 29
- References
- 1
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 8 · 2023: 4 · 2022: 6 · 2021: 3 · 2020: 1
- FWCI
- 2.43
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- knowledge base question answering, transformer models
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