Metadata-Version: 2.1
Name: ontolearn
Version: 0.0.1
Summary: Ontolearn is an open-source software library for structured machine learning in Python.Ontolearn includes modules for processing knowledge bases, inductive logic programming and ontology. engineering.
Home-page: UNKNOWN
Author: Caglar Demir
Author-email: caglardemir8@gmail.com
License: UNKNOWN
Description: # Ontolearn
        
        Ontolearn is an open-source software library for structured machine learning in Python. Ontolearn includes modules for processing knowledge bases, representation learning, inductive logic programming and ontology engineering.
        
        - [Framework](#Framework)
            - [KnowledeBase](#Knowledgebase)
            - [Concept](#Concept)        
            - [Refinement Operator](#Refinements)
                    
        - [Installation](#installation)
        
        ## Current Installation
        ```
        1) git clone https://github.com/dice-group/OntoPy.git
        2) conda create -n temp python=3.6.2 # Or be sure that your have Python => 3.6.
        3) conda activate temp
        4) python ontolearn/setup.py install
        # After you receive this Finished processing dependencies for OntoPy==0.0.1
        5) python -c "import ontolearn"
        ```
        ## Later Installation
        
        ```python
        pip install ontolearn
        ```
        
        ## Usage
        
        ```python
        from ontolearn import KnowledgeBase, Refinement
        
        kb = KnowledgeBase(path='data/family-benchmark_rich_background.owl')
        rho = Refinement(kb)
        for refs in enumerate(rho.refine(kb.thing)):
            print(refs)
        ```
        
        ```python
        from ontolearn import KnowledgeBase,SampleConceptLearner
        kb = KnowledgeBase(path='data/family-benchmark_rich_background.owl')
        model = SampleConceptLearner(knowledge_base=kb,iter_bound=100,verbose=False)
        p = {'http://www.benchmark.org/family#F10M173', 'http://www.benchmark.org/family#F10M183'}
        n = {'http://www.benchmark.org/family#F1F5', 'http://www.benchmark.org/family#F1F7'}
        model.predict(pos=p, neg=n)
        model.show_best_predictions(top_n=10)
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
