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OWL2Vec4OA

OWL2Vec4OA by Sevinj Teymurova

OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment

Features

OWL2Vec4OA is an extension of the ontology embedding system OWL2Vec* which exposes a CLI with two subcommands after installation, which allows you to perform two main programs. You can also run the two original python programs without installation (see the requirements in setup.py <https://github.com/KRR-Oxford/OWL2Vec-Star/blob/master/setup.py>__).

Installation command::

    $ make install

Standalone

This command will embed two ontologies and their corresponding mappings. It can be configured by the configuration file default1.cfg. See the examples and comments in default1.cfg for the usage.

Running program::

Option 1

1. Install Python 3: https://python.org/downloads
2. Install setuptools: https://pypi.org/project/setuptools
3. Run this command in the terminal:

    $ python setup.py install

4. Run this command in the terminal:

    $ python OWL2Vec_Standalone1.py --config_file default1.cfg

Option 2

1. Install Python 3: https://python.org/downloads
2. Install pip: https://pip.pypa.io/en/stable/installation
3. Install library dependicies in requirements: pip install -r requirements_owl2vec.txt
4. Run jupyter notebook:

    jupyter_notebook_owl2vec4oa.ipynb 

Note: Different from the experimental codes, the standalone command has implemented all OWL ontology relevant procedures in python with Owlready, but it also allows the user to use pre-calculated annotations/axioms/entities/projection to generate the corpus.

Parameters to change when running the code with help of configuration file

1. ontology_file1
2. ontology_file2
3. mapping
4. confidence_threshold
5. seed_entities_on_mapping
6. cache_dir
7. walk_depth

Implementations

Code under:

1. OWL2Vec_Standalone1.py and owl2vec4mappings.py implements merging two ontologies into one single projection RDF graph, axiom/annotations/seed entities documents generation and model training. Note, the code owl2vec4mappings.py is used to compile jupyter_notebook_owl2vec4oa.ipynb
2. owl2vec_star/rdf2vec/graph.py implements creating the Knowledge Graph
3. owl2vec_star/lib/RDF2Vec_Embed.py implements reading projection RDF graph, adding mapping entities and their confidence values into the Knowledge Graph(kg) to be able to extract walks for model training.
4. owl2vec_star/rdf2vec/walkers/mapping4vec.py implements bias sampling walking strategy over RDF graphs
5. LogmapIntersectionAml.py - implements Intersection of mappings produced by LogMap and AML
6. LogmapUnionAml.py - implements Union of mappings produced by LogMap and AML
7. rdf2tsv.py- RDF to TSV
8. txt2tsv.py - TXT to TSV
9. 2023ns2nn_2.py To run the code on HPC, using SNOMED2NCIT.NEOPLAS data with the configuration file `2023ns2nn_2.cfg. (version 0.0.1, last access: 07/2024) with revision.

Publications

• Sevinj Teymurova, Ernesto Jimenez-Ruiz, Tillman Weyde, Jiaoyan Chen. OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment. Submitted to a conference: arXiv.

• Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, and Ian Horrocks. OWL2Vec: Embedding of OWL ontologies*. Machine Learning, Springer, 2021. PDF Springer Collection Codes in package or folder

Case Studies

Experiments conducted on the OAEI's Bio-ML track.

Results

You can find the computed embeddings in our zenodo repository