Leveraging Lexical Semantic Information for Learning Concept-Based Multiple Embedding Representations for Knowledge Graph Completion
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
TransC combines structural information with entity concepts to improve KGE models and introduces a novel type of semantic similarity to measure the distinctness of entity semantics using concept information.
“The TransC [149] model combines structural information with entity concepts (referring to the categories of entities) to improve KGE models and introduces a novel type of semantic similarity to measure the distinctness of entity semantics by using the concept information.”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [149]
Cites
Introduction to Statistical Relational Learning
(2007) · doi:10.7551/mitpress/7432.001.0001 · Cited by 1,643 · 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
Flagged first, then by citations
Cited by
Sorted by citations
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, Y., Liu, Y., Zhang, H., & Xie, H.
- Affiliations
- China Academy of Electronics and Information Technology (CN)
- University of Science and Technology of China (CN)
- Corresponding author
- Wang, Yashen
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 382-397
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2963510636
- Cited by
- 36
- References
- 2
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 6 · 2023: 8 · 2022: 6 · 2021: 7 · 2020: 3
- FWCI
- 1.53
- 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
- knowledge graph completion, entity concept, knowledge graph embedding
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
