ontolearn.learners.sparql_query_learner
=======================================

.. py:module:: ontolearn.learners.sparql_query_learner


Classes
-------

.. autoapisummary::

   ontolearn.learners.sparql_query_learner.SPARQLQueryLearner


Module Contents
---------------

.. py:class:: SPARQLQueryLearner(learning_problem: ontolearn.learning_problem.PosNegLPStandard, endpoint_url: str, max_number_of_filters: int = 3, use_complex_filters: bool = True)

   Learning SPARQL queries: Given a description logic concept (potentially generated by a concept learner),
   try to improve the fittness (e.g., F1) of the corresponding SPARQL query.

       Attributes:
           name (str): Name of the model = 'SPARQL Query Learner'
           endpoint_url (string): The URL of the SPARQL endpoint to use
           max_number_of_filters (int): Limit the number of filters combined during the improvement process
           learning_problem (PosNegLPStandard): the learning problem (sets of positive and negative examples)
           uses_complex_filters (bool): Denotes whether the learner uses complex filters
                                       (i.e., makes use of the values of data properties) to improve the quality
           _root_var (str): The root variable to be used in the OWL2SPARQL conversion
           _possible_filters (List[str]): A list of possible FILTERs to use to improve the quality


   .. py:attribute:: __slots__
      :value: ('endpoint_url', 'max_number_of_filters', 'uses_complex_filters', 'learning_problem',...



   .. py:attribute:: name
      :value: 'SPARQL Query Learner'



   .. py:attribute:: endpoint_url
      :type:  str


   .. py:attribute:: max_number_of_filters
      :type:  int


   .. py:attribute:: learning_problem
      :type:  ontolearn.learning_problem.PosNegLPStandard


   .. py:attribute:: uses_complex_filters
      :type:  bool


   .. py:method:: learn_sparql_query(ce: owlapy.class_expression.OWLClassExpression)