Entity Enabled Relation Linking
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EERL discovers implicit relations by using domains and ranges of properties in the knowledge graph, but it cannot handle relation linking through relation paths.
“For instance, EERL discovers the implicit relations by using the domains and ranges of properties in KG, but it cannot handle the issue of RL through relation paths.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Pan et al., 2019
The available Abstract describes EERL as using entities extracted from questions and DBpedia entities to compute relation candidates. Searches for property domains and ranges, implicit relations, and relation paths found no passage addressing those specific claims. The available copy therefore does not establish either the proposed mechanism or the asserted limitation. (LLM, conf=0.72)
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Cites
Large-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
- Pan, J. Z., Zhang, M., Singh, K., Harmelen, F. V., Gu, J., & Zhang, Z.
- Affiliations
- University of Aberdeen (GB)
- Wuhan University of Science and Technology (CN)
- Vrije Universiteit Amsterdam (NL)
- Corresponding author
- Pan, Jeff Z.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 523-538
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2980646235
- Cited by
- 20
- References
- 1
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 4 · 2022: 5 · 2021: 6 · 2020: 2
- FWCI
- 1.24
- Type
- conference paper
- Language
- en
- Open access
- green
- research.vu.nl
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- VU Research Portal (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- knowledge graphs, question answering, named entity recognition, entity linking, DBpedia, natural language queries
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.
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