.png){kind=icon}
.png){kind=icon}
Return
3DRTE: 3D Rotation Embedding in Temporal Knowledge Graph
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3036897.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Temporal knowledge graph (TKG) embedding has received increasing attention in the academia. However, most existing methods are extensions of traditional translation models. Due to their intrinsic limitations, it is often difficult for such methods to effectively model essential characteristics of TKG, namely three basic relation patterns including symmetry/antisymmetry, inversion, and composition. In this paper, a new 3-Dimensional Rotation Temporal Embedding (3DRTE) method is proposed. Firstly, we selectively fuse temporal and relational features of fact triples by taking advantages of self-attention mechanism in processing sequential information. Then, entities are modelled as points in three-dimensional space, and the relations are interpreted as two isoclinic rotations between entities with Quaternion. Experimental results on several public datasets show that our method obtains state-of-the-art results.
Keywords:
Quaternions
Three-dimensional displays
Solid modeling
Protocols
Training
Adaptation models
Fuses
Temporal knowledge graph
3D rotation embedding
self-attention
quaternion
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.