TravSHACL 1.3.2

A SHACL validator capable of planning the traversal and execution of the validation of a shape schema to detect violations early.

We present Trav-SHACL, a SHACL engine capable of planning the traversal and execution of a shape schema in a way that invalid entities are detected early and needless validations are minimized. Trav-SHACL reorders the shapes in a shape schema for efficient validation and rewrites target and constraint queries for fast detection of invalid entities. The shape schema is validated against an RDF graph accessible via a SPARQL endpoint.

How to run Trav-SHACL?

If you are looking for examples or want to reproduce the results reported in our WWW '21 paper, checkout the eval-www2021 branch.

Note: The current version of Trav-SHACL does not produce a validation report that complies with the SHACL specification. We will add this feature in the future.

Prerequisites

The following guides assume:

• Your shape schema is placed in ./shapes
• There is a SPARQL endpoint running that you can connect to, in this example it is http://localhost:14000/sparql
  • The endpoint is running in Docker
  • It is connected to the Docker network semantic-web
  • Its name is endpoint1
  • The port 8890 of the Docker container is mapped to port 14000 of the host

Parameters

• -d schemaDir (necessary) - path to the directory containing the shape files
• endpoint (necessary) - URL of the endpoint the shape schema will be validated against
• graphTraversal (necessary) - defines the graph traversal algorithm to be used, is one of [BFS, DFS]
• outputDir (necessary) - directory to be used for storing the result files, has to end on /
• --heuristics (necessary) - used to determine the seed shape
  • TARGET if shapes with a target definition should be prioritized, otherwise omit
  • prioritize in- or outdegree of shapes, one of [IN, OUT] or to be omitted
  • prioritize shapes based on their number of constraints, one of [BIG, SMALL] or to be omitted
• --selective (optional) - use more selective queries for constraint queries
• --orderby (optional) - sort the results of all SPARQL queries, ensures the same order in the result logs over several runs
• --outputs (optional) - creates one file each for violated and validated targets, otherwise only statistics and traces will be stored
• -m (optional) - maximum number of entities in FILTER or VALUES clause of a SPARQL query, default: 256
• -j / --json (optional) - indicates that the SHACL shape schema is expressed in JSON

Features

The current implementation of Trav-SHACL does not cover all features of the complete SHACL language. The following is a list of what is supported:

• simple cardinality constraints, i.e., sh:minCount and sh:maxCount)
• relaxed shape-based constraints, i.e., sh:qualifiedValueShape with sh:qualifiedMinCount and sh:qualifiedMaxCount
• simple SPARQL constraints, i.e., sh:sparql with sh:select
  • sh:prefixes is currently not implemented, i.e., the query needs to use full URIs
  • sh:message is ignored, i.e., the message is not included in the result
  • only $this is supported as placeholder

The following is a list of some of the more important features that are not yet covered:

• sh:or
• sh:node
• sh:datatype
• sh:value
• and others

Run with Docker

In order to connect to the SPARQL endpoint, it must be accessible from within the Docker container. There shouldn't be anything to configure if you use a public endpoint like DBpedia or Wikidata. However, if you run your own dockerized SPARQL endpoints, make sure that the endpoint and the Trav-SHACL container are connected to the same Docker network, in this example it is called semantic-web.

# Preparation
docker build -t travshacl .
docker run --name trav-shacl -v $(pwd)/shapes:/shapes -v $(pwd)/results:/results --network=semantic-web -d travshacl

# Run the Validation
docker exec -it trav-shacl bash -c "python3 main.py -d /shapes http://endpoint1:8890/sparql /results/ DFS --heuristics TARGET IN BIG --orderby --selective --outputs"

Run with Python3

pip3 install -r requirements.txt
python3 main.py -d ./shapes http://localhost:14000/sparql ./results/ DFS --heuristics TARGET IN BIG --orderby --selective --outputs

Trav-SHACL as Python3 Library

Trav-SHACL is available on PyPI, you can install it via the following command:

python3 -m pip install travshacl

After installing Trav-SHACL from PyPI you can use it like in this example:

from TravSHACL import parse_heuristics, GraphTraversal, ShapeSchema

schema_dir = './shapes'
endpoint_url = 'http://localhost:14000/sparql'
graph_traversal = GraphTraversal.DFS  # BFS is also available
prio_target = 'TARGET'  # shapes with target definition are preferred, alternative value: ''
prio_degree = 'IN'  # shapes with a higher in-degree are prioritized, alternative value 'OUT'
prio_number = 'BIG'  # shapes with many constraints are evaluated first, alternative value 'SMALL'
output_dir = './results/'

shape_schema = ShapeSchema(
    schema_dir=schema_dir,  # directory where the files containing the shapes definitions are stored
    schema_format='SHACL',  # do not change this value unless you are using the legacy JSON format
    endpoint=endpoint_url,  # the URL of the SPARQL endpoint to be evaluated, alternatively an RDFLib graph can be passed
    graph_traversal=graph_traversal,  # graph traversal algorithm used for planning the shapes order
    heuristics=parse_heuristics(prio_target + ' ' + prio_degree + ' ' + prio_number),  # heuristics to be used for planning the evaluation order
    use_selective_queries=True,  # use more selective constraint queries, alternative value: False
    max_split_size=256,  # maximum number of entities in FILTER or VALUES clause
    output_dir=output_dir,  # directory where the output files will be stored
    order_by_in_queries=False,  # sort the results of SPARQL queries in order to ensure the same order across several runs
    save_outputs=True  # save outputs to output_dir, alternative value: False
)

result = shape_schema.validate()  # validate the SHACL shape schema
print(result)

How to run the Test Suite?

In order to run the test suite, you need to install the production and development dependencies.

pip3 install -r requirements.txt -r requirements-dev.txt

Afterwards, start the Docker container with the test data.

docker-compose -f tests/docker-compose.yml up -d

Finally, you can run the tests by executing the following command.

pytest

Publications

1. Mónica Figuera, Philipp D. Rohde, Maria-Esther Vidal. Trav-SHACL: Efficiently Validating Networks of SHACL Constraints. In Proceedings of the Web Conference 2021 (WWW '21), April 19-23, 2021, Ljubljana, Slovenia. https://doi.org/10.1145/3442381.3449877, Experiment Scripts, Preprint