Datasets:

EuropeanParliament
/

EuroVoc

This tool automates the collection of EU legislative documents from the CELLAR (Commission des Européennes Legislation Archive and Repository) system. It extracts document text in multiple formats (PDF, DOCX, XHTML) and enriches documents with Eurovoc thesaurus concept labels and their corresponding unique identifiers.

Features

SPARQL-based document discovery: Queries the EU Publications Office SPARQL endpoint to find documents by date range
Multi-format text extraction: Supports PDF, DOCX, DOC, and XHTML document formats
Eurovoc enrichment: Automatically adds Eurovoc concept labels and IDs to documents
Parallel processing: Uses multiprocessing for faster document text extraction
Caching: Joblib-based caching to avoid redundant API calls and document downloads
Robust error handling: Comprehensive logging of failures with detailed error messages
Compressed output: Handles gzip-compressed JSONL files

Directory Structure

ep_registry_scrapper/
├── README.md                    # This file
├── requirements.txt             # Python dependencies
├── update.py                    # Main scraper script
├── add_id.py                    # Post-processing script to add Eurovoc IDs
├── get_eurovoc_dict.py          # Utility to fetch and cache Eurovoc data
├── process_all.sh               # Bash wrapper for complete pipeline
└── query.j2                      # Jinja2 template for SPARQL queries (required)

Installation

Prerequisites

Python 3.7+
pip or conda

Setup

Clone or download the repository
Install dependencies:

pip install -r requirements.txt

Note: You'll need a query.j2 template file that defines the SPARQL query for document discovery. This should contain a Jinja2 template with {{ start }} and {{ end }} date variables.

Usage

1. Extract Documents and Text

Extract documents from CELLAR and their text content:

python update.py <output_prefix> <num_days>

Arguments:

output_prefix: Prefix for output JSONL files (organized by month)
num_days: Number of days to look back from today

Example:

python update.py output_ 30

This will create files like output_2025-11.jsonl for the last 30 days.

2. Fetch Eurovoc Dictionary

Generate a pickled dictionary mapping Eurovoc terms to their IDs:

python get_eurovoc_dict.py

Output: eurovoc_dict.pkl

3. Add Eurovoc IDs to Documents

Enrich existing JSONL files with Eurovoc concept IDs:

python add_id.py <input_file> <output_file>

Arguments:

input_file: Input JSONL file (can be /dev/stdin for piping)
output_file: Output JSONL file

Example:

python add_id.py documents.jsonl documents_with_ids.jsonl

4. Batch process to add IDs to multiple jsonls

If you need to add Eurovoc IDs to multiple JSONL files, use a shell loop:

for file in output_*.jsonl; do
  python add_id.py "$file" "${file%.jsonl}_with_ids.jsonl"
done

5. Generate Eurovoc metrics

Create language barplots, document length per language boxplots

python analyse.py

Complete Pipeline

The main workflow is simply:

python update.py output_ 30

This extracts documents and text for the last 30 days.

If Eurovoc IDs were not appended during extraction (or want to update a already generated file), you can enrich the documents afterwards:

Fetch the Eurovoc dictionary (one-time setup):
```
python get_eurovoc_dict.py
```

Add IDs to your JSONL files:

python add_id.py documents.jsonl documents_with_ids.jsonl

Or use the bash wrapper to automate the entire pipeline:
```
bash process_all.sh
```

This ensures all steps are executed in the correct order.

Updating the IDs of all documents is recommended as labels and IDs can change over time

Output Format

The scraper produces JSONL (JSON Lines) files with the following structure per document:

{
  "url": "https://publications.europa.eu/...",
  "title": "Document Title",
  "date": "2025-11-20",
  "lang": "en",
  "formats": ["pdf", "xhtml"],
  "eurovoc_concepts": ["term1", "term2", ...],
  "eurovoc_concepts_ids": ["id1", "id2", ...],
  "text": "Extracted document text..."
}

Logging

All operations are logged to collect.log in the script directory. This includes:

Document processing status
Missing Eurovoc terms
Download and parsing failures
API errors

Monitor logs for issues:

tail -f collect.log

API Dependencies

EU Publications Office SPARQL Endpoint: https://publications.europa.eu/webapi/rdf/sparql
Eurovoc Dataset API: http://publications.europa.eu/resource/dataset/eurovoc

Performance Considerations

Caching: Downloads are cached for 2 hours to avoid redundant API calls
Parallel Processing: Uses 16 worker processes for text extraction
Rate Limiting: Consider adding delays if processing large date ranges

Dependencies

See requirements.txt for the complete list. Key packages:

requests: HTTP client for API calls
beautifulsoup4: HTML/XML parsing
pdfminer.six: PDF text extraction
docx2txt: Microsoft Word document extraction
jinja2: Template rendering for SPARQL queries
joblib: Caching and parallel processing
tqdm: Progress bars
xmltodict: XML parsing