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

Note: Python 3.6+ is required.

Note that we only support computation on GPU (CUDA). We have tested our code with Nvidia Titan Xp (12GB) and RTX 2080Ti (11GB). 6 or 8GB of memory should also be enough though we couldn't test them.

1. Install PyTorch. We have verified that version 1.2.0 works.
2. Install other requirements: pip install -r requirements.txt
3. Run chmod +x preprocess.sh && ./preprocess.sh
4. Install spacy model: python -m spacy download en && python -m spacy download en_core_web_md
5. Preprocess datasets (do these steps for each dataset in {FB15k, FB15k-237, YAGO3-10}):
   1. python main_literal.py dataset {FB15k, FB15k-237, YAGO3-10} epochs 0 process True
   2. Numerical literals: python preprocess_num_lit.py --dataset {FB15k, FB15k-237, YAGO3-10}
   3. Text literals: python preprocess_txt_lit.py --dataset {FB15k, FB15k-237, YAGO3-10}

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

For DistMult+LiteralE with numerical and textual literals:

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

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