This is the official pytorch implementation of the BoxEL for the description logic EL++

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Deep LearningBoxEL
Overview

BoxEL: Box EL++ Embedding

This is the official pytorch implementation of the BoxEL for the description logic EL++. BoxEL++ is a geometric approach based on box embedding to generate embeddings for the description logic EL++

Requiriments

You need CUDA installed to use a GPU, and need to install python libraries with:

pip install -r requirements.txt

Data

We have preprocessed all the data in /data directory. In particular, we have normalized the ontologies into normal forms and splited the data into train/valid/test sets.

For original data, refer https://bio2vec.cbrc.kaust.edu.sa/data/elembeddings/el-embeddings-data.zip for protein-protein interaction and https://github.com/kracr/EmELpp for subsumption reasoning.

How to run Box EL++

We provide two ways to run our BoxEL++ embeddings: python scripts and jupyter notebooks. Since different dataset/tasks have different features, we provide specific files for each datasts/tasks

Jupyter notebooks

We provided specific jupyter notebooks files for all the tasks used in our paper.

e.g, to run and visualize our family domain example, simply open and run

./notebooks/BoxEL-ToyFamily.ipynb

You could get the the following results

drawing

Python scripts

We also provided python scripts to run the tasks.

e.g. to run BoxEL on Gene Ontology, simply run

python scripts/BoxEL-GO.py 

The settings of the used hyperparameters is given in the python files.

Pretrained models

We provided some pretrained models in ./models/

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