NeuralKG
https://github.com/zjukg/NeuralKG
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
| Components | Models |
| KGEModel | TransE(2013)， TransH(2014)， TransR(2015)， ComplEx(2016)， DistMult(2014)， RotatE(2019)， ConvE(2018)， BoxE(2020)， CrossE(2019)， SimplE(2018) |
| GNNModel | RGCN(2017)， KBAT(2019)， CompGCN(2020)， XTransE(2020) |
| RuleModel | ComplEx-NNE+AER(2018)， RUGE(2018)， IterE(2019) |
知识表示学习常用的方法
来自第四范式技术分享
https://yeu.h5.xeknow.com/sl/3BzAvx
https://github.com/AutoML-Research
Triplet-based models
Translational Distance Model（TDM）
2013，TransE
2014，TransH
2019，TotatE
Neural Network Model（NNM）
2014，MLP
2018，ConvE
2020，RSN
BiLinear Model（BLM）
2011，RESCAL，关系为方阵
2014，DistMult，关系为对角矩阵
2016，CompIEx，
2017，Analogy
2018，SimpIE
2019，QuatE
2020，AutoSF，BLMs – unified form
2021，AutoSF+，An AutoML approach to design bilinear scoring functions
Bilinear models are the best choice in triplet-based models
Relational path
2015，PTransE
2019，RSN，Models long-term information well, but is limited in modeling short-term information
2020，Insterstellar，A NAS approach to recurrently process the relational paths
The relational paths contain richer information than triplets
GCN-variants
2017，R-GCN
2020，CompGCN
2020，GraIL，Enclosing subgraph -> Entity labeling – > GNN scoring
2021，KE-GCN
2022，RED-GNN
