TP-RotatE: A knowledge graph representation learning method combining path information and rules to capture complex relational patterns.
Representation learning on a knowledge graph (KG) aims to map entities and relationships into a low-dimensional vector space. Traditional methods for representation learning have predominantly focused on the structural aspects of triples within the KG. While existing approaches have endeavored to in...
Saved in:
| Main Authors: | Xinliang Liu, Yanyan Shi, Yushi Xu, Yanzhao Ren |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0324059 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing Biomedical Knowledge Representation Through Knowledge Graphs
by: Sebastian Chalarca, et al.
Published: (2024-05-01) -
DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights
by: Haji Gul, et al.
Published: (2024-01-01) -
PathoGraph: A Graph-Based Method for Standardized Representation of Pathology Knowledge
by: Peiliang Lou, et al.
Published: (2025-05-01) -
Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining
by: Zhiwei Cheng, et al.
Published: (2025-05-01) -
Interdependent-path Recurrent Embedding for Knowledge Graph-aware Recommendation
by: Xiao Sha, et al.
Published: (2025-06-01)