.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/syntactic/plot_1_onto2vec.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_syntactic_plot_1_onto2vec.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_syntactic_plot_1_onto2vec.py:


Onto2Vec
===========

This example corresponds to the paper `Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations <https://doi.org/10.1093/bioinformatics/bty259>`_. 

This method is an approach to learn numerical representations (embeddings) of (biomedical) ontologies by representing ontology axioms as text sequences and applying an unsupervised learning algorithm such as Word2Vec. Onto2Vec uses an ontology reasoner to infer new axioms as 
a preprocessing step. The algorithm is tested on the protein-protein interaction task.

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For this algorithm, we need three components:

- The reasoner
- The corpus generator
- The Word2Vec model

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.. code-block:: Python



    import mowl
    mowl.init_jvm("20g")

    from mowl.datasets.builtin import PPIYeastSlimDataset
    from mowl.corpus import extract_and_save_axiom_corpus
    from mowl.owlapi import OWLAPIAdapter
    from mowl.reasoning import MOWLReasoner

    from org.semanticweb.elk.owlapi import ElkReasonerFactory
    from java.util import HashSet

    from gensim.models.word2vec import LineSentence
    from gensim.models import Word2Vec

    import os








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Inferring new axioms
--------------------

Onto2Vec uses an ontology reasoner to infer new axioms as a preprocessing step. In the original
paper, the authors used the HermiT reasoner. For this example, we use the ELK reasoner.

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.. code-block:: Python


    dataset = PPIYeastSlimDataset()

    reasoner_factory = ElkReasonerFactory()
    reasoner = reasoner_factory.createReasoner(dataset.ontology)
    mowl_reasoner = MOWLReasoner(reasoner)








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We wrap the reasoner into the :class:`MOWLReasoner <mowl.reasoning.base.MOWLReasoner>` class \
in order to use some shortcuts the mOWL
provides such as:

- inferring subclass axioms
- inferring equivalent class axioms
- inferring disjoint axioms (not applicable for this example since we use ELK reasoner)

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.. code-block:: Python


    classes = dataset.ontology.getClassesInSignature()
    subclass_axioms = mowl_reasoner.infer_subclass_axioms(classes)
    equivalent_class_axioms = mowl_reasoner.infer_equivalent_class_axioms(classes)








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We can now add the inferred axioms to the ontology.

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.. code-block:: Python


    adapter = OWLAPIAdapter()
    manager = adapter.owl_manager

    axioms = HashSet()
    axioms.addAll(subclass_axioms)
    axioms.addAll(equivalent_class_axioms)

    manager.addAxioms(dataset.ontology, axioms)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <java object 'org.semanticweb.owlapi.model.parameters.ChangeApplied'>



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Generating the corpus and training the model
-----------------------------------------------

Now that we have an extended ontology, we can generate the corpus out of it. After that, we
can train the Word2Vec model.

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.. code-block:: Python


    extract_and_save_axiom_corpus(dataset.ontology, "onto2vec_corpus.txt")

    sentences = LineSentence("onto2vec_corpus.txt")
    model = Word2Vec(sentences, vector_size=5, window=2, min_count=1, workers=4, epochs=2)








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Cleaning up memory

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.. code-block:: Python


    os.remove("onto2vec_corpus.txt")








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 15.920 seconds)

**Estimated memory usage:**  499 MB


.. _sphx_glr_download_examples_syntactic_plot_1_onto2vec.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_1_onto2vec.ipynb <plot_1_onto2vec.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_1_onto2vec.py <plot_1_onto2vec.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_1_onto2vec.zip <plot_1_onto2vec.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_