rdf-fingerprinter 0.2.1a1

Find out kind of data shapes your RDF dataset instantiates.

Understand the structure of your RDF data at a glance using automatically built application profiles and spot differences between dataset profiles.

An application profile, in this context, is the set of data shapes 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

1. Create a project folder.
2. Prepare the input data by running this SPARQL query 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.

An example project is available here. It is is based on HTML5/CSS template using Pub-CSS 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.

Envisioned development

• architectural sketch
• future features

Licence

RDF Fingerprinter is freely distributable under the terms of the GNU GPLv3