SmartDataAnalytics/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

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.