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A package for ontology engineering with deep learning.

Changelog 📰

  • [2023 March - ] Release \(\textsf{DeepOnto}\) v0.6.x

    • Working on integrating BERTSubs into DeepOnto.
    • Deploy the deeponto.lama modules with improved ontology verbalisation.
  • [2023 Jan - Feb] Release \(\textsf{DeepOnto}\) v0.5.x

    • Shift to the src/ layout and fix PyPI issues.
    • Add deeponto.onto.verbalisation for ontology concept verbalisation.
    • Rebuild the whole package based on the OWLAPI; remove owlready2 from the essential dependencies.


\(\textsf{DeepOnto}\) aims to provide tools for implementing deep learning models, constructing resources, and conducting evaluation for various ontology engineering purposes.



\(\textsf{DeepOnto}\) relies on OWLAPI version 4 (written in Java) for ontologies.

We use what has been implemented in mOWL that uses JPype to bridge Python and Java Virtual Machine (JVM).


According to mOWL, the current integration with OWLAPI can work on Linux or Mac OS but not Windows.


\(\textsf{DeepOnto}\) relies on Pytorch for deep learning framework.

Configure Pytorch installation with CUDA support using, for example:

pip3 install torch torchvision torchaudio --extra-index-url

Basic usage of Ontology does not rely on GPUs, but for efficient deep learning model training, please make sure torch.cuda.is_available() returns True.

Install from PyPI

Other dependencies are specified in setup.cfg and requirements.txt which are supposed to be installed along with deeponto.

# requiring Python>=3.8
pip install deeponto

Use Git Repository

One can git clone the repository without installing through PyPI and install the dependencies manually by:

pip install -r requirements.txt

Main Features

Extending the OWLAPI

\(\textsf{DeepOnto}\) extends the OWLAPI library for ontology processing and reasoning, and also for better integration with deep learning modules. The base classes that extend the OWLAPI functionalities are Ontology and OntologyReasoner. Examples of how to use them can be found here.


BERTMap [1] is a BERT-based ontology matching (OM) system originally developed in repo but is now maintained in \(\textsf{DeepOnto}\). See how to use BERTMap in this tutorial.


Bio-ML [2] is an OM resource that has been used in the Bio-ML track of the OAEI. See instructions of how to use Bio-ML.


BERTSubs [3] is a system for ontology subsumption prediction. We are working on transforming its original experiment codes to this project.



Copyright 2021 Yuan He (KRR-Oxford). All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.


  • [1] Yuan He‚ Jiaoyan Chen‚ Denvar Antonyrajah and Ian Horrocks. BERTMap: A BERT−Based Ontology Alignment System. In Proceedings of 36th AAAI Conference on Artificial Intelligence 2022 (AAAI-2022). /arxiv/ /aaai/
  • [2] Yuan He‚ Jiaoyan Chen‚ Hang Dong, Ernesto Jiménez-Ruiz, Ali Hadian and Ian Horrocks. Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. The 21st International Semantic Web Conference (ISWC-2022, Best Resource Paper Candidate). /arxiv/ /iswc/
  • [3] Jiaoyan Chen, Yuan He, Yuxia Geng, Ernesto Jiménez-Ruiz, Hang Dong and Ian Horrocks. Contextual Semantic Embeddings for Ontology Subsumption Prediction. World Wide Web Journal (to appear). /arxiv/
  • [4] Yuan He‚ Jiaoyan Chen, Ernesto Jiménez-Ruiz, Hang Dong and Ian Horrocks. Language Model Analysis for Ontology Subsumption Inference. 2023 (Under review). /arxiv/

Please report any bugs or queries by raising a GitHub issue or sending emails to the maintainer (Yuan He) through:

Last update: January 24, 2023
Created: January 11, 2023
GitHub: @Lawhy   Personal Page: