CatE: Lattice preserving $\mathcal{ALC}$ ontology embeddings.

DOI

https://doi.org/10.1007/978-3-031-71167-1_19

Abstract

Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying semantics of OWL ontologies is expressed using Description Logics (DLs). Initial approaches to generate embeddings relied on constructing a graph out of ontologies, neglecting the semantics of the logic therein. Recent semantic-preserving embedding methods often target lightweight DL languages like $\mathcal{EL}^{++}$, ignoring more expressive information in ontologies. Although some approaches aim to embed more descriptive DLs like $\mathcal{ALC}$, those methods require the existence of individuals, while many real-world ontologies are devoid of them. We propose an ontology embedding method for the $\mathcal{ALC}$ DL language that considers the lattice structure of concept descriptions. We use connections between DL and Category Theory to materialize the lattice structure and embed it using an order-preserving embedding method. We show that our method outperforms state-of-the-art methods in several knowledge base completion tasks.

Dependencies

• Python >= 3.8
• mOWL

Set up environment

cd catE
conda env create -f environment.yml
conda activate cate

Getting the data

The data can be obtained from the following Zenodod repository: https://zenodo.org/records/13766937 After downloading, decompresss the file with the following command:

tar -xzvf use_cases.tar.gz

Running the model

• ORE1

run_cat_membership.py --batch_size=32768 --emb_dim=200 --loss_type=normal --lr=0.0001 --margin=1 --num_negs=4 --use_case=ore1

• GO

python run_cat_completion.py --batch_size=32768 --emb_dim=200 --loss_type=normal --lr=1e-05 --margin=1 --num_negs=2 --use_case=go -ns

• FoodOn

python run_cat_completion.py --batch_size=8192 --emb_dim=200 --loss_type=normal --lr=0.0001 --margin=1 --num_negs=2 --use_case=foodon -ns

• PPI

python run_cat_ppi.py --batch_size=65536 --emb_dim=200 --loss_type=normal --lr=0.0001 --margin=0.1 --num_negs=2 -ns

Citation

@InProceedings{10.1007/978-3-031-71167-1_19,
author="Zhapa-Camacho, Fernando
and Hoehndorf, Robert",
editor="Besold, Tarek R.
and d'Avila Garcez, Artur
and Jimenez-Ruiz, Ernesto
and Confalonieri, Roberto
and Madhyastha, Pranava
and Wagner, Benedikt",
title="Lattice-Preserving {\$}{\$}{\backslash}mathcal {\{}ALC{\}}{\$}{\$}Ontology Embeddings",
booktitle="Neural-Symbolic Learning and Reasoning",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="355--369",
isbn="978-3-031-71167-1"
}