Metadata-Version: 2.1
Name: rdf-fingerprinter
Version: 0.2.1a1
Summary: Find out kind of data shapes your RDF dataset instantiates.
Home-page: https://github.com/meaningfy-ws/rdf-fingerprinter
Author: Eugeniu Costetchi
Author-email: costezki.eugen@gmail.com
Maintainer: Eugeniu Costetchi
Maintainer-email: costezki.eugen@gmail.com
License: UNKNOWN
Description: 
        **Understand** the structure of your RDF data at a glance using automatically built **application profiles** and spot differences between dataset profiles. 
        
        An [_application profile_](https://en.wikipedia.org/wiki/Application_profile), in this context, is the set of [_data shapes_](https://www.w3.org/2014/data-shapes/wiki/Main_Page) designed for a particular purpose acting as constraints on how the data are instantiated and so can be used to validate the data.
        
        _Fingerprinting_ is the action of generating, or rather, guessing, the application profile applied to a particular dataset. This is an inductive process of reconstructing the data shape for each class instantiated in the dataset. 
        
        # Installation
        RDF fingerprinter may be installed with pip as follows. (Because ,this project is still in Alpha stage, the installation is available for the moment from sources only.)
         
        ```
        git clone https://github.com/costezki/RDF-fingerprint-diff.git
        cd RDF-fingerprint-diff
        pip install . 
        ```
        
        This project currently supports python 3.6 or later.  
        
        # Getting started
        
        At the moment the fingerprinter is able to deliver the core functionality which is generate the fingerprint of an RDF dataset structured as an application profile. To launch it follow the following steps (In the future this process will be simplified). The detailed documentation is available [here](https://github.com/costezki/RDF-fingerprint-diff/wiki/Application-profile-project)
        
        1. Create a project folder.    
        2. Prepare the input data by running [this SPARQL query](https://github.com/costezki/RDF-fingerprint-diff/blob/master/resources/query/fingerprint.rq) on the target dataset(s).    
        3. (optional) Tweak the _configuration.json_ file. 
        4. Run the fingerprinter in the project folder.
        
        Details on each of the steps are available [here](https://github.com/costezki/RDF-fingerprint-diff/wiki/Application-profile-project).
        
        An example project is available [here](https://github.com/costezki/RDF-fingerprint-diff/tree/master/examples/fingerprinter_jinja/pub_css_ap). It is is based on HTML5/CSS template using [Pub-CSS](https://github.com/thomaspark/pubcss) styling.  Please feel free to copy and modify this project as needed. The document template (in /fragments sub-folder) is built using [Jinja2 templating language](http://jinja.pocoo.org/docs/2.10/).   
        
        # Envisioned development 
        * [architectural sketch](https://github.com/costezki/RDF-fingerprint-diff/wiki/Specifications)
        * [future features](https://github.com/costezki/RDF-fingerprint-diff/wiki/Future-features)
        
        # Licence 
        _RDF Fingerprinter_ is freely distributable under the terms of the [GNU GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html)
Keywords: rdf,application profile,data shape,statistics,fingerprint,sparql,linked-data
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Utilities
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Natural Language :: English
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
