.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/syntactic/plot_2_opa2vec.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_2_opa2vec.py>`
        to download the full example code.

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

.. _sphx_glr_examples_syntactic_plot_2_opa2vec.py:


OPA2Vec
===========

This example corresponds to the paper `OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction <https://doi.org/10.1093/bioinformatics/bty933>`_. 

This method is an extension of Onto2Vec that apart from formal knowldege (i.e. axioms) it also uses informal knowledge such as entity metadata (i.e. synonyms, definitions, etc.)

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For this algorithm, we need four components:

- The reasoner
- The corpus generator
- The annotations 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, extract_and_save_annotation_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
--------------------

OPA2Vec 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, "opa2vec_corpus.txt")
    extract_and_save_annotation_corpus(dataset.ontology, "opa2vec_corpus.txt", mode="a")

    sentences = LineSentence("opa2vec_corpus.txt")
    model = Word2Vec(sentences, vector_size=5, window=2, min_count=1, workers=4, epochs=2)








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Cleaning up the memory

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.. code-block:: Python


    os.remove("opa2vec_corpus.txt")








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

   **Total running time of the script:** (0 minutes 14.199 seconds)

**Estimated memory usage:**  405 MB


.. _sphx_glr_download_examples_syntactic_plot_2_opa2vec.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_2_opa2vec.ipynb <plot_2_opa2vec.ipynb>`

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

      :download:`Download Python source code: plot_2_opa2vec.py <plot_2_opa2vec.py>`

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

      :download:`Download zipped: plot_2_opa2vec.zip <plot_2_opa2vec.zip>`


.. only:: html

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

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