Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text
Open accessAbstract
Ahmad Sakor, Isaiah Onando Mulang’, Kuldeep Singh, Saeedeh Shekarpour, Maria Esther Vidal, Jens Lehmann, Sören Auer. 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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Many relations in natural language are implicitly expressed and may not link to a single relation in the knowledge graph because of ambiguity in natural language and incompleteness of the knowledge graph.
“Due to the ambiguity of NL and the incompleteness of KG, many relations in NL are implicitly expressed, and may not link to a single relation in KG, which challenges the current methods.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Sakor et al., 2019
The passages supplied from our copy contain bibliographic metadata, not the paper’s discussion. Searches for implicitly expressed relations, natural-language ambiguity, knowledge-graph incompleteness, and linking to a single relation returned no substantive passage addressing the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredFalcon and SLING perform poorly for other kinds of implicit relations and cannot handle relation linking through relation paths.
“However, they perform poorly for other kinds of implicit relations, and cannot handle RL through relation paths either.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Sakor et al., 2019
The available indexed passages are bibliographic metadata (no section or page), not discussion of Falcon or SLING. Searches for implicit relations, poor performance, and relation paths found no passage addressing either claimed limitation. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThe existing tool Falcon has shown accurate results on entity linking, around 0.83 F1-score on the LC-QuAD dataset as reported.
“which has shown accurate results on entity linking (around 0.83 of F1-score on the LC-QuAD dataset as reported).”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Sakor et al., 2019
The supplied passages and indexed search results contain only bibliographic metadata, with no results section or page addressing Falcon’s entity-linking performance. Searches for Falcon, LC-QuAD, and 0.83 F1-score found no passage that verifies the reported figure. (LLM, conf=0.72)
Experimental — accuracy not yet measuredExisting relation linking methods perform well on several benchmark datasets but are still challenged by implicit relations prevalent in the real world.
“However, according to (Sakor et al., 2019), while existing RL methods perform well on several benchmark datasets, they are still challenged by implicit relations prevalent in the real world.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Sakor et al., 2019
The supplied passages are bibliographic metadata, with no section or page discussing relation-linking performance. Searches for benchmark performance and implicit relations returned no substantive full-text passage; the only topical text returned was an abstract, which cannot serve as evidence under the stated rubric. (LLM, conf=0.70)
Experimental — accuracy not yet measuredFalcon enhances entity and relation linking through cross-KG entity and relation alignment and basic principles of English morphology.
“Falcon (Sakor et al., 2019) enhances entity and relation linking through cross-KG entity and relation alignment and basic principles of English morphology.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Sakor et al., 2019
The supplied passages are bibliographic metadata, and searches for cross-KG alignment, English morphology, and Falcon's method returned no passage from the paper's body. The indexed abstract addresses the claim, but the audit rules exclude abstracts as evidence, so the claimed mechanism cannot be verified from eligible passages. (LLM, conf=0.69)
Experimental — accuracy not yet measured
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Cites
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015 · cited 3× · see citing works
“Similarly, for relation labels, the local KG is enriched with traditional linguistic resources such as Oxford dictionary (OED, 1989), and semantic dictionaries like WordNet (Miller, 1995a) to provide synonyms, derived word forms, etc.”
BackgroundMethodLarge-scale Simple Question Answering with Memory Networks
Bordes, A., Usunier, N., Chopra, S., & Weston, J. (2015) · arXiv · doi:10.48550/arxiv.1506.02075 · Cited by 528 · see citing works
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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
- Sakor, A., Onando Mulang’, I., Singh, K., Shekarpour, S., Esther Vidal, M., Lehmann, J., & Auer, S.
- 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
- 2336-2346
- OpenAlex
- W2946606955
- Cited by
- 86
- References
- 2
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 9 · 2023: 12 · 2022: 13 · 2021: 18 · 2020: 16 · 2019: 11
- FWCI
- 5.55
- 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)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- entity linking, short text, linguistic features, named entity recognition, relation extraction
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