vliviu/LiteralE

Knowledge Graph Embeddings learned from the structure and literals of knowledge graphs

LiteralE

Knowledge Graph Embeddings learned from the structure and literals of knowledge graphs.

ArXiv link for the paper: Incorporating Literals into Knowledge Graph Embeddings

Credits

This work is built on top of Tim Dettmers' ConvE codes: https://github.com/TimDettmers/ConvE.

Getting Started

1. Install PyTorch
2. Install other requirements: pip install -r requirements.txt
3. Preprocess datasets: chmod +x preprocess.sh && ./preprocess.sh && python wrangle_KG.py
4. Preprocess literals: python preprocess_num_lit.py

Reproducing Paper's Experiments

For DistMult+LiteralE and ComplEx+LiteralE:

python main_literal.py dataset {FB15k, FB15k-237, YAGO3-10} model {DistMult, ComplEx} input_drop 0.2 embedding_dim 100 batch_size 128 epochs 100 lr 0.001 process True

For ConvE+LiteralE:

python main_literal.py dataset {FB15k, FB15k-237, YAGO3-10} model ConvE input_drop 0.2 hidden_drop 0.3 feat_drop 0.2 embedding_dim 200 batch_size 128 epochs 100 lr 0.001 process True

NB: For base models, replace main_literal.py with main.py.