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pySHACL

A Python validator for SHACL.

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This is a pure Python module which allows for the validation of RDF graphs against Shapes Constraint Language (SHACL) graphs. This module uses the rdflib Python library for working with RDF and is dependent on the OWL-RL Python module for OWL2 RL Profile based expansion of data graphs.

This module is developed to adhere to the SHACL Recommendation:

Holger Knublauch; Dimitris Kontokostas. Shapes Constraint Language (SHACL). 20 July 2017. W3C Recommendation. URL: https://www.w3.org/TR/shacl/ ED: https://w3c.github.io/data-shapes/shacl/

Community for Help and Support

The SHACL community has a discord server for discussion of topics around SHACL and the SHACL specification.

Use this invitation link: https://discord.gg/RTbGfJqdKB to join the server

There is a #pyshacl channel for discussion of this python library, and you can ask for general SHACL help too.

Installation

Install with PIP (Using the Python3 pip installer pip3)

$ pip3 install pyshacl

Or in a python virtualenv (these example commandline instructions are for a Linux/Unix based OS)

$ python3 -m virtualenv --python=python3 --no-site-packages .venv
$ source ./.venv/bin/activate
$ pip3 install pyshacl

To exit the virtual enviornment:

$ deactivate

Command Line Use

For command line use: (these example commandline instructions are for a Linux/Unix based OS)

$ pyshacl -s /path/to/shapesGraph.ttl -m -i rdfs -a -j -f human /path/to/dataGraph.ttl

Where

System exit codes are: 0 = DataGraph is Conformant 1 = DataGraph is Non-Conformant 2 = The validator encountered a RuntimeError (check stderr output for details) 3 = Not-Implemented; The validator encountered a SHACL feature that is not yet implemented.

Full CLI Usage options:

$ pyshacl -h
$ python3 -m pyshacl -h
usage: pyshacl [-h] [-s [SHACL]] [-e [ONT]] [-i {none,rdfs,owlrl,both}] [-m]
               [-im] [-a] [-j] [-it] [--abort] [--allow-info] [-w]
               [--max-depth [MAX_DEPTH]] [-d]
               [-f {human,table,turtle,xml,json-ld,nt,n3}]
               [-df {auto,turtle,xml,json-ld,nt,n3}]
               [-sf {auto,turtle,xml,json-ld,nt,n3}]
               [-ef {auto,turtle,xml,json-ld,nt,n3}] [-V] [-o [OUTPUT]]
               [--server]
               DataGraph

PySHACL 0.27.0 command line tool.

positional arguments:
  DataGraph             The file containing the Target Data Graph.

optional arguments:
  --server              Ignore all the rest of the options, start the HTTP Server.
  -h, --help            show this help message and exit
  -s [SHACL], --shacl [SHACL]
                        A file containing the SHACL Shapes Graph.
  -e [ONT], --ont-graph [ONT]
                        A file path or URL to a document containing extra
                        ontological information. RDFS and OWL definitions from this 
                        are used to inoculate the DataGraph.
  -i {none,rdfs,owlrl,both}, --inference {none,rdfs,owlrl,both}
                        Choose a type of inferencing to run against the Data
                        Graph before validating.
  -m, --metashacl       Validate the SHACL Shapes graph against the shacl-
                        shacl Shapes Graph before validating the Data Graph.
  -im, --imports        Allow import of sub-graphs defined in statements with
                        owl:imports.
  -a, --advanced        Enable features from the SHACL Advanced Features
                        specification.
  -j, --js              Enable features from the SHACL-JS Specification.
  -it, --iterate-rules  Run Shape's SHACL Rules iteratively until the
                        data_graph reaches a steady state.
  --abort               Abort on first invalid data.
  --allow-info, --allow-infos
                        Shapes marked with severity of Info will not cause
                        result to be invalid.
  -w, --allow-warning, --allow-warnings
                        Shapes marked with severity of Warning or Info will
                        not cause result to be invalid.
  --max-depth [MAX_DEPTH]
                        The maximum number of SHACL shapes "deep" that the
                        validator can go before reaching an "endpoint"
                        constraint.
  -d, --debug           Output additional verbose runtime messages.
  --focus [FOCUS]       Optional IRIs of focus nodes from the DataGraph, the shapes will
                        validate only these node. Comma-separated list.
  --shape [SHAPE]       Optional IRIs of a NodeShape or PropertyShape from the SHACL
                        ShapesGraph, only these shapes will be used to validate the
                        DataGraph. Comma-separated list.
  -f {human,table,turtle,xml,json-ld,nt,n3}, --format {human,table,turtle,xml,json-ld,nt,n3}
                        Choose an output format. Default is "human".
  -df {auto,turtle,xml,json-ld,nt,n3}, --data-file-format {auto,turtle,xml,json-ld,nt,n3}
                        Explicitly state the RDF File format of the input
                        DataGraph file. Default="auto".
  -sf {auto,turtle,xml,json-ld,nt,n3}, --shacl-file-format {auto,turtle,xml,json-ld,nt,n3}
                        Explicitly state the RDF File format of the input
                        SHACL file. Default="auto".
  -ef {auto,turtle,xml,json-ld,nt,n3}, --ont-file-format {auto,turtle,xml,json-ld,nt,n3}
                        Explicitly state the RDF File format of the extra
                        ontology file. Default="auto".
  -V, --version         Show PySHACL version and exit.
  -o [OUTPUT], --output [OUTPUT]
                        Send output to a file (defaults to stdout).
  --server              Ignore all the rest of the options, start the HTTP
                        Server. Same as `pyshacl_server`.

Python Module Use

For basic use of this module, you can just call the validate function of the pyshacl module like this:

from pyshacl import validate
r = validate(data_graph,
      shacl_graph=sg,
      ont_graph=og,
      inference='rdfs',
      abort_on_first=False,
      allow_infos=False,
      allow_warnings=False,
      meta_shacl=False,
      advanced=False,
      js=False,
      debug=False)
conforms, results_graph, results_text = r

Where:

Some other optional keyword variables available on the validate function:

Return value:

Python Module Call

You can get an equivalent of the Command Line Tool using the Python3 executable by doing:

$ python3 -m pyshacl

Errors

Under certain circumstances pySHACL can produce a Validation Failure. This is a formal error defined by the SHACL specification and is required to be produced as a result of specific conditions within the SHACL graph that leads to the inability to complete the validation. If the validator produces a Validation Failure, the results_graph variable returned by the validate() function will be an instance of ValidationFailure. See the message attribute on that instance to get more information about the validation failure.

Other errors the validator can generate:

Unlike ValidationFailure, these errors are not passed back as a result by the validate() function, but thrown as exceptions by the validation engine and must be caught in a try ... except block. In the case of ShapeLoadError and ConstraintLoadError, see the str() string representation of the exception instance for the error message along with a link to the relevant section in the SHACL spec document.

Focus Node Filtering, and Shape Selection

PySHACL v0.27.0 and above has two powerful new features:

SPARQL Remote Graph Mode

PySHACL now has a built-in SPARQL Remote Graph Mode, which allows you to validate a data graph that is stored on a remote server.

Integrated OpenAPI-3.0-compatible HTTP REST Service

PySHACL now has a built-in validation service, exposed via an OpenAPI3.0-compatible REST API.

Due to the additional dependencies required to run, this feature is an optional extra.

You must first install PySHACL with the http extra option enabled:

$ pip3 install -U pyshacl[http]

When that is installed, you can start the service using the by executing the CLI entrypoint:

$ pyshacl --server
# or
$ pyshacl_server
# or
$ python3 -m pyshacl server
# or
$ docker run --rm -e PYSHACL_SERVER=TRUE -i -t docker.io/ashleysommer/pyshacl:latest

By default, this will run the service on localhost address 127.0.0.1 on port 8099.

To view the SwaggerUI documentation for the service, navigate to http://127.0.0.1:8099/docs/swagger and for the ReDoc version, go to http://127.0.0.1:8099/docs/redoc.

To view the OpenAPI3 schema see http://127.0.0.1:8099/docs/openapi.json

Configuring the HTTP REST Service

Windows CLI

Pyinstaller can be used to create an executable for Windows that has the same characteristics as the Linux/Mac CLI program. The necessary .spec file is already included in pyshacl/pyshacl-cli.spec. The pyshacl-cli.spec PyInstaller spec file creates a .exe for the pySHACL Command Line utility. See above for the pySHACL command line util usage instructions.

See the PyInstaller installation guide for info on how to install PyInstaller for Windows.

Once you have pyinstaller, use pyinstaller to generate the pyshacl.exe CLI file like so:

$ cd src/pyshacl
$ pyinstaller pyshacl-cli.spec

This will output pyshacl.exe in the dist directory in src/pyshacl.

You can now run the pySHACL Command Line utility via pyshacl.exe. See above for the pySHACL command line util usage instructions.

Docker

Pull the official docker image from Dockerhub: docker pull docker.io/ashleysommer/pyshacl:latest

Or build the image yourself, from the PySHACL repository with docker build . -t pyshacl.

You can now run PySHACL inside a container; but you need to mount the data you want to validate. For example, to validate graph.ttl against shacl.ttl, run :

docker run --rm -i -t --mount type=bind,src=`pwd`,dst=/data pyshacl -s /data/shacl.ttl /data/graph.ttl

Compatibility

PySHACL is a Python3 library. For best compatibility use Python v3.8 or greater. Python3 v3.7 or below is not supported and this library does not work on Python v2.7.x or below.

PySHACL is a PEP518 & PEP517 project, it uses pyproject.toml and poetry to manage dependencies, build and install.

For best compatibility when installing from PyPI with pip, upgrade to pip v20.0.2 or above.

Features

A features matrix is kept in the FEATURES file.

Changelog

A comprehensive changelog is kept in the CHANGELOG file.

Benchmarks

This project includes a script to measure the difference in performance of validating the same source graph that has been inferenced using each of the four different inferencing options. Run it on your computer to see how fast the validator operates for you.

License

This repository is licensed under Apache License, Version 2.0. See the LICENSE deed for details.

Contributors

See the CONTRIBUTORS file.

Citation

DOI: 10.5281/zenodo.4750840 (For all versions/latest version)

Contacts

Project Lead: Nicholas Car Senior Experimental Scientist CSIRO Land & Water, Environmental Informatics Group Brisbane, Qld, Australia nicholas.car@csiro.au http://orcid.org/0000-0002-8742-7730

Lead Developer: Ashley Sommer Informatics Software Engineer CSIRO Land & Water, Environmental Informatics Group Brisbane, Qld, Australia Ashley.Sommer@csiro.au https://orcid.org/0000-0003-0590-0131