Capturing Semantic and Syntactic Information for Link Prediction in Knowledge Graphs
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RW-LMLM takes into account both order information and the random walk algorithm, and it has the capability of dealing with underlying semantic information.
“RW-LMLM [91] takes into account both the order information and random walk algorithm, and it has the capability of dealing with underlying semantic information.”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [91]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
SimplE Embedding for Link Prediction in Knowledge Graphs
Kazemi, S. M., & Poole, D. (2018) · arXiv · doi:10.48550/arxiv.1802.04868 · Cited by 476 · see citing works
STransE: a novel embedding model of entities and relationships in knowledge bases
Nguyen, D. Q., Sirts, K., Qu, L., & Johnson, M. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1054 · Cited by 177 · see citing works
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A Survey on Knowledge Graph Embeddings for Link Prediction
Wang, M., Qiu, L., & Wang, X. (2021) · Symmetry · doi:10.3390/sym13030485 · Cited by 212 · see citing works
- Authors
- Wang, C., Yan, M., Yi, C., & Sha, Y.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Sha, Ying
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 664-679
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2980969032
- Cited by
- 4
- References
- 3
- Citations by year
- 2022: 2 · 2021: 2
- FWCI
- 0.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- link prediction, knowledge graphs, random walks, language models, semantic information, syntactic information, missing links, parameter efficiency
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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