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Documentation generator for DPV vocabularies

Downloads the CSV data for DPV and other vocabularies (such as DPV-GDPR), converts it to RDF serialisations, and generates HTML documentation using the W3C ReSpec template.

Requires: python3 and modules rdflib, rdflib-jsonld, jinja2

The Data Privacy Vocabulary (DPV) is available at https://www.w3.org/ns/dpv and its repository is at https://github.com/dpvcg/dpv.

NOTE: This repository only holds the scripts required to generate the documentation. This is NOT the repository for the vocabulary itself. This is to keep the vocabulary and documentation in separate repositories.

Quick Summary

There are 3 scripts to executre for each of the three tasks.

./001_download_vocab_in_csv.py will download the CSV data from a Google Sheets document and store it in the vocab_csv path specified. The outcome will be a CSV file for each sheet.

./002_parse_csv_to_rdf.py will create RDF serialisations for DPV using data from CSV. It will create different serialisation files for each 'module' and also for DPV combined.

In between steps 2 and 3, there can be a series of tests done to ensure the RDF is generated correctly. For this, some basic SHACL constraints are defined in shacl_shapes.

./003_generate_respec_html.py will generate HTML documentation for DPV and DPV-GDPR from RDF.

How everything works

Downloading CSV data

This uses the Google Sheet export link to download the sheet data in CSV form. Needs specifying the document ID in DPV_DOCUMENT_ID variable and listing the sheet name(s) in DPV_SHEETS. The default save path for CSV is vocab_csv.

Converting to RDF

This uses rdflib to generate the RDF data from CSV. It uses DPV_CSV_FILES to retrieve classes and properties from the CSV and render them in RDF serialisations. Namespaces are manually represented in the top of the document and are automatically handled in text as URI references. Serialisations to be produced are registered in RDF_SERIALIZATIONS variable.

The variables for CSV inputs and RDF outputs are:

  • IMPORT_CSV_PATH defines where the CSV files are stored, with default value ./vocab_csv
  • EXPORT_DPV_PATH defines where the DPV rdf files are stored, with default value ./vocab_dpv
  • EXPORT_DPV_MODULE_PATH defines where the DPV module files are stored, with default value ./vocab_dpv/modules
  • EXPORT_DPV_GDPR_PATH defines where the DPV-GDPR files are stored, with default value ./vocab_dpv_gdpr

There are three main classes responsible for generation of metadata:

  • add_common_triples_for_all_terms will add common metadata for each term, such as label, description, author, and so on
  • add_triples_for_classes will add metadata for classes such as subclass
  • add_triples_for_properties will add metadata for properties such as domain, range, sub-property

Generating HTML documentation

This uses jinja2 to render the HTML file from a template. The data is loaded using a module called rdform which is meant to provide ORM features and convenience features over RDF data.

The variables for RDF inputs and HTML outputs are:

  • IMPORT_DPV_MODULES_PATH defines where the RDF for DPV modules are loaded from, with default value ./vocab_dpv/modules
  • IMPORT_DPV_GDPR_PATH defines where the RDF for DPV-GDPR module is loaded from, with default value ./vocab_dpv_gdpr
  • EXPORT_DPV_HTML_PATH defines where the output HTML for DPV documentation is stored, with default value ./docs, the generated file is index.html
  • EXPORT_DPV_GDPR_HTML_PATH defines where the output HTML for DPV-GDPR documentation is stored, with default value ./docs/dpv-gdpr, the generated file is index.html

The general flow of steps in the script is along the following lines:

  1. Load RDF instances from a module file with the load_data function.
  2. This creates a RDF graph using rdflib and extracts classes and properties from it in separate variables as {module}_classes and {module}_properties
  3. Create HTML using a jinja2 template, which is located in jinja2_resources. The tempalte for dpv is template_dpv.jinja2.
  4. The template uses a macro to repeat the same table and metadata records for each module and term. The macro is defined in macro_term_table.jinja2. The template file itself contains the other information such as headlines and paragraphs.

Testing using SHACL

The folder shacl_shapes holds the constraints in shapes.ttl to verify the vocabulary terms contain some basic annotations. The script verify.py executes the SHACL validator (currently hardcoded to use the TopBraid SHACL binary as shaclvalidate.sh), retrieves the results, runs a SPARQL query on them to get the failing nodes and messages.

The script uses DATA_PATHS to declare what data files should be validated. Currently, it will only validate Turtle (.tt) files for simplicity as all files are duplicate serialisations of each other. The variable SHAPES declares the list of shape files to use. For each folder in DATA_PATHS, the script will execute the SHACL binary to check constraints defined in each of the SHAPES files.

To execute the tests, and to use the TopBraid SHACL binary, download the latest release from maven, extract it somewhere and note the path of the folder. Export SHACLROOT in the shell the script is going to run in (or e.g. save it in the bash profile) to the path of the folder. To be more precise, $SHACLROOT/shaclvalidate.sh should result in the binary being executed.

The output of the script lists the data and shapes files being used in the validation process, the number of errors found, and a list of data nodes and the corresponding failure message.

FAQ

  1. Fixing an error in the vocabulary terms i.e. term label, property, annotation --> Make the changes in the Google Sheet, and run scripts to download CSV, parse RDF, and generate HTML
  2. Fixing an error in serialisation e.g. rdf:Property is defined as rdfs:Propety --> Make the changes in the 002 script for generating RDF, and generate HTML
  3. Changing content in HTML documentation e.g. change motivation paragraph --> Make the changes in the relevant template and generate HTML