literal-embeddings

Neural Regression with Embeddings for Numeric Attribute ( Literals ) Prediction in Knowledge Graphs

Installation and Requirements

Create Conda Environment:

git clone https://github.com/dice-group/literal-embeddings.git

cd literal-embeddings

conda create --name litem  python=3.10 && conda activate litem

Install Dice embedding Framework

pip install -r requirements.txt

Dataset with Literals

The zip file KGs.zip contains all the datasets for experiments. For the first use,

 unzip KGs.zip 

Experiment Types

There are primarily three different types of experiments available with this framework:

Training of Literal Embedding Model with Pre-trained KGE model

To train the Literal Embedding model with Pre-trained KGE models, set the flag --literal_training in the args. Use the flags such as --lit_lr and --num_epochs_lit for learning rate and number of epochs for training the Literal Embedding Model. For training with the Liteal Embedding model, provide the path to the folder with weights and configurations of the KGE model using --pretrained_kge_path.

This should support KGE model trained with this framework as well as from dice-embedding framework.

The literal dataset should be present inside the Literal folder of the respective KGs dataset for the link prediction task. e.g, KGs/Family/literals should contain numeric triples for Family dataset.

Important: Use the --pretrained_kge_path to provide the path for the pre-trained KGE model with configurations and model weights. The Literal Embedding model takes the Knowledge Graph and Embedding dim from the pre-trained KGE configs.

python main.py --dataset_dir KGs/Family --lit_lr 0.05 --literal_training --pretrained_kge_path "Experiments/test_dir" --lit_epochs 200

Training of Knowledge Graph Embedding with Literal Embedding Model model

The flag combined training must be used to perform combined training of any KGE model and Literal Embedding Model. --combined_training. We support all the KGE models available in the dice-embedding framework.

python main.py  --model Keci --dataset_dir KGs/Family --lr 0.05 --embedding_dim 128 --num_epochs 256 --combined_training

Training of Knowledge Graph Embedding model

To perform the training of the stand-alone Knowledge Graph Embedding model, just provide the model name, KGs dir and so on.

python main.py  --model Keci --dataset_dir KGs/Family --lr 0.05 --embedding_dim 128 --num_epochs 256 

If you wish to save the KGE model for Literal embedding training ( or other purposes), use save_experiment argument

python main.py  --model Keci --dataset_dir KGs/Family --lr 0.05 --embedding_dim 128 --num_epochs 256 --save_experiment

If you want to save the experiment on a desired path, provide the path in the CLI as:

python main.py  --model Keci --dataset_dir KGs/Family --lr 0.05 --embedding_dim 128 --num_epochs 256 --save_experiment --full_storage_path "Experiments/test_dir"

You can then provide such experiment path to train the Literal embedding Model.