Model list

BaseModel class is the base class of all models in CogKGE. BaseModel class organizes code into three basic sections: (1) forward function for training, (2) embedding function for getting the embedding of entities and relations, (3) scoring function for computing the score of triples. Model module consists of four parts: translation distance models, semantic matching models, graph neural network-based models and transformer-based models. We summarize the models in the following table:

Category Model Conference Paper
Translation Distance Models TransE NIPS 2013 Translating embeddings for modeling multi-relational data
TransH AAAI 2014 Knowledge Graph Embedding by Translating on Hyperplanes
TransR AAAI 2015 Learning Entity and Relation Embeddings for Knowledge Graph Completion
TransD ACL 2015 Knowledge Graph Embedding via Dynamic Mapping Matrix
TransA AAAI 2015 TransA: An Adaptive Approach for Knowledge Graph Embedding
BoxE NIPS 2020 BoxE: A Box Embedding Model for Knowledge Base Completion
PairRE ACL 2021 PairRE: Knowledge Graph Embeddings via Paired Relation Vectorss
Semantic Matching Models RESCAL ICML 2011 A Three-Way Model for Collective Learning on Multi-Relational Data
DistMult ICLR 2015 Embedding Entities and Relations for Learning and Inference in Knowledge Bases
SimpleIE NIPS 2018 SimplE Embedding for Link Prediction in Knowledge Graphs
TuckER ACL 2019 TuckER: Tensor Factorization for Knowledge Graph Completion
RotatE ICLR 2019 RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Graph Neural Network-based Models R-GCN ESWC 2018 Modeling Relational Data with Graph Convolutional Networks
CompGCN ICLR 2020 Composition-based Multi-Relational Graph Convolutional Networks
Transformer-based Models HittER EMNLP 2021 HittER: Hierarchical Transformers for Knowledge Graph Embeddings
KEPLER TACL 2021 KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation