---
# System prepended metadata

title: '[Paper Study]KGE: A Survey of Approaches and Applications'

---

# [Paper Study]KGE: A Survey of Approaches and Applications
https://persagen.com/files/misc/Wang2017Knowledge.pdf
### Details
- KG embeddings steps:
    1. Entity, relation represenstaions
    2. score function
    3. learning, optimize representations
### Embedding techniques:
#### Translational distance models
==distance-based scoring functions==

    
    - TransE:
        - distance between h+r, t
        - flaw: 1-N, N-1, N-N
    - TransH:
        - model relation as vector distance on a hyperplane.

    - TransR: project to relation space
    - TransD: decompsose projection matrix into 2 vectors.
    - TransM: manifold
    - TransA: adaptive Mahalanobis distance
    - TransF: ...

#### Semantic Mathing Models
==similarity based scoring function==
latent representation 




![](https://i.imgur.com/nsgQlFD.png)


- semantic matching models
    - RESCAL
    - 





