drm-tools 1.1.0a1

Package for document representation model

Jialuo Chen Adrià Molina

Author-email: {oriolrt, jchen, amolina}@cvc.uab.cat License: MIT License Keywords: python,documents,classification,knowledge representation,neo4j Classifier: Development Status :: 5 - Production/Stable Classifier: Intended Audience :: Developers Classifier: License :: OSI Approved :: MIT License Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3 :: Only Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Operating System :: Unix Classifier: Operating System :: MacOS :: MacOS X Classifier: Operating System :: Microsoft :: Windows Description-Content-Type: text/x-rst License-File: LICENSE Dynamic: author Dynamic: author-email Dynamic: classifier Dynamic: description Dynamic: description-content-type Dynamic: home-page Dynamic: keywords Dynamic: license Dynamic: license-file Dynamic: summary

Document Representation Model (drm-tools)

Graph-based document representation library with Neo4j and an in-memory NetworkX backend.

Model documents as graphs where nodes represent document objects (text regions, figures, pages) and edges capture their relationships. The library supports semantic entity definitions, weak nodes with cascade delete, foreign key validation, vector search, and reusable example datasets for tutorials.

Features

• Two backends: Full Neo4j integration (Neo4jGraph) or in-memory NetworkX (NetworkXGraph) for testing and tutorials

• WeakNode hierarchy: Child entities with composite primary keys and automatic cascade delete through parent-child edges

• ON DELETE strategies: CASCADE, RESTRICT, SET NULL – choose the deletion semantics that fit your use case

• Semantic entities: Domain-specific node types such as IndividuPadro, LlocPadro, and Fotografia

• FK validation: Foreign key constraints on relations prevent dangling references

• Query and filtering: Secondary index on scalar properties, multi-filter search with intersection/union, debug snapshots

• Vector search (NetworkX only): HNSW-based ANN indexing on node properties with cosine, l2, and ip distance spaces

• RDF/OWL ontology conversion: Generate Python entity classes from RDF/OWL ontologies (RiC-O, etc.)

Installation

Install from PyPI:

pip install drm-tools

Or install from source in development mode:

git clone https://github.com/CVC-DAG/drm-tools.git
cd drm-tools
pip install -e .

Register the recommended Jupyter kernel for tutorials:

python -m ipykernel install --user --name drm-tool --display-name "Python (drm-tool)"

Quick Start

from drm import NetworkXGraph, Node, WeakNode

# In-memory backend -- no database required
graph = NetworkXGraph()

# Create a document hierarchy
doc = Node(pk={"doc": "DOC-001"}, main_label="Document")
graph.insertNode(doc)

section = WeakNode(parent=doc, pk={"section": 1}, main_label="Section")
graph.insertNode(section, insert_parent=True)

page = WeakNode(parent=section, pk={"page": 1}, main_label="Page")
graph.insertNode(page, insert_parent=True)

# Query the graph
print("Nodes:", graph.get_node_ids())
print("Edges:", graph.get_edges())
graph.close()

Tutorial Notebooks

Runnable Jupyter notebooks in docs/tutorials/notebooks/. Each notebook installs the package automatically from the latest release when run.

You can also view them rendered in the hosted documentation.

Getting Started

• intro_basics – Minimal end-to-end workflow: insert nodes, create WeakNode hierarchies

• querying_and_filtering – Query operations: get_node(), find_nodes(), property filtering

Interactive Demos

• weaknodes_interactive – Build hierarchies with an interactive widget panel

• vector_search – HNSW vector indexing and nearest-neighbor search

• delete_strategies – Compare CASCADE, RESTRICT, SET NULL strategies

Datasets

• karate_club – Zachary Karate Club (34 members)

• movies – Movie-domain graph (actors, genres, films)

• game_of_thrones – Character-house graph

• bibliography_openalex – OpenAlex bibliographic references with citations

Ontologies

• generating_classes_from_owl – Generate Python entity classes from RDF/OWL ontologies

RDF/OWL Ontology Conversion

Generate Python entity classes from RDF/OWL ontologies in one step:

from drm.rdf_schema import download_ontology_and_convert

# Downloads, converts to YAML, and generates Python classes
output_path = download_ontology_and_convert(
    "https://raw.githubusercontent.com/ICA-EGAD/RiC-O/master/ontology/current-version/RiC-O_1-1.rdf",
    "rico",
    output_dir="drm/"
)
# Generates drm/rico_entities.py (677 classes from RiC-O)

Step by step:

from drm.rdf_schema import download_ontology, rdf_to_yaml
from drm.schema_gen import generate_classes

# 1. Download ontology
ont_path = download_ontology(url, output_dir="ontologies/")

# 2. Convert RDF to YAML DRM
yaml_str = rdf_to_yaml(ont_path, "my_db")

# 3. Generate Python classes
py_source = generate_classes(yaml_str)

# 4. Write file
with open("drm/entities_my_db.py", "w") as f:
    f.write(py_source)

The pipeline maps OWL constructs to DRM:

• owl:Class – Node label

• rdfs:subClassOf – WeakNode hierarchy (parent)

• owl:DatatypeProperty – Node properties

• owl:ObjectProperty – Relationships

• owl:hasKey – Primary key fields

• rdfs:comment – Class docstring

Example Dataset Loaders (drm.exemples)

The package includes ready-to-run loaders for common graph domains:

• drm.exemples.networkx_karate – Karate Club graph (NetworkX classic)

• drm.exemples.networkx_bibliografia – Bibliographic references from OpenAlex

• drm.exemples.neo4j_movies – Movie-domain graph

• drm.exemples.neo4j_got – Game of Thrones character-house graph

Command-line loader

python -m drm.exemples --dataset karate --backend networkx
python -m drm.exemples --dataset all --backend both --quiet

Programmatic usage

from drm import NetworkXGraph
from drm.exemples import load_karate_club, load_bibliografia_openalex

graph = NetworkXGraph()
print(load_karate_club(graph))
print(load_bibliografia_openalex(graph, query="graph database", per_page=15))
graph.close()

Configuration

DRM uses environment variables for Neo4j connections. Multiple targets are supported via the NEO4J_TARGET selector:

# Default target
export NEO4J_DEV_URL=bolt://dev-host:7687
export NEO4J_DEV_USER=neo4j
export NEO4J_DEV_PASSWORD=your_dev_password
export NEO4J_DEV_DATABASE=neo4j

# Custom target
export NEO4J_TARGET=LOCAL
export NEO4J_LOCAL_URL=bolt://localhost:7687
export NEO4J_LOCAL_USER=neo4j
export NEO4J_LOCAL_PASSWORD=your_password
export NEO4J_LOCAL_DATABASE=neo4j

Running Tests

python -m pytest test/ -v

Three test levels:

• Unit (-m unit) – 43 tests, ~2s, fast, no graph store

• Integration (-m integration) – 215 tests, ~3s, NetworkXGraph (in-memory)

• Neo4j (-m slow) – 44 tests, ~10s, Neo4j (requires real DB)

Skip Neo4j tests: pytest test/ -v -m "not slow"

Documentation

• Hosted docs: https://cvc-dag.github.io/drm-tools/

• Source docs: docs/ – Sphinx documentation source

Generate HTML docs with Sphinx:

cd docs
sphinx-build -b html . _build/html

Authors and Contributors

• Oriol Ramos Terrades

• Jialuo Chen

• Adrià Molina

Acknowledgements

This work has been partially supported by the Spanish project PID2021-126808OB-I00, Ministerio de Ciencia e Innovación, the Departament de Cultura of the Generalitat de Catalunya, and the CERCA Program / Generalitat de Catalunya. Adrià Molina is funded with the PRE2022-101575 grant provided by MCIN / AEI / 10.13039 / 501100011033 and by the European Social Fund (FSE+).