infoextract-cidoc 0.1.1

Classful Ontology for Life-Events Information Extraction

infoextract-cidoc (COLLIE)

Classful Ontology for Life-Events Information Extraction

A developer-friendly toolkit for working with the CIDOC CRM v7.1.3 in modern data workflows. infoextract-cidoc provides complete Pydantic models (99 classes, 322 properties), LangStruct-powered AI extraction, Markdown renderers, and Cypher emitters that bridge the gap between conceptual rigor and developer usability.

Why infoextract-cidoc?

Cultural heritage and information extraction projects often need a CRM-compliant backbone without the overhead of RDF stacks. infoextract-cidoc:

• Keeps the conceptual rigor of CIDOC CRM
• Provides lean, open-world Pydantic validation
• Outputs formats directly usable by LLMs (Markdown) and LPGs (Cypher)
• Prioritizes ergonomics and performance for real-world extraction pipelines
• Zero RDF/OWL/JSON-LD dependencies

Quick Start

For a comprehensive getting started guide, see QUICKSTART.md

Installation

pip install infoextract-cidoc

# With GraphForge graph database integration:
pip install infoextract-cidoc[graphforge]

# Using uv:
uv add infoextract-cidoc

AI Extraction (New Pipeline)

from infoextract_cidoc.extraction import LangStructExtractor, resolve_extraction, map_to_crm_entities
from infoextract_cidoc.io.to_markdown import to_markdown, MarkdownStyle

# Extract from text (set GOOGLE_API_KEY or LANGSTRUCT_DEFAULT_MODEL env var)
extractor = LangStructExtractor()
lite_result = extractor.extract(
    "Albert Einstein was born on March 14, 1879, in Ulm, Germany. "
    "He won the Nobel Prize in Physics in 1921."
)

# Resolve to stable UUIDs
extraction_result = resolve_extraction(lite_result)

# Map to CIDOC CRM entities
crm_entities, crm_relations = map_to_crm_entities(extraction_result)

# Render to Markdown
for entity in crm_entities:
    print(to_markdown(entity, MarkdownStyle.CARD))

CRM Models (No AI Needed)

from infoextract_cidoc.models.generated.e_classes import EE22_HumanMadeObject
from infoextract_cidoc.io.to_markdown import to_markdown, MarkdownStyle
from infoextract_cidoc.io.to_cypher import generate_cypher_script

# Create a CRM entity (string IDs are automatically converted to UUIDs)
vase = EE22_HumanMadeObject(
    id="obj_001",
    label="Ancient Greek Vase",
    type=["E55:Vessel", "E55:Ceramic"]
)

# Render as Markdown
markdown = to_markdown(vase, MarkdownStyle.CARD)

# Generate Cypher for Neo4j/Memgraph
cypher = generate_cypher_script([vase])

CLI

# Extract entities from text
infoextract-cidoc extract --text "Marie Curie was born in Warsaw in 1867."

# Extract from file
infoextract-cidoc extract --file biography.txt --output ./output/

# Run complete workflow
infoextract-cidoc workflow --file biography.txt --all --output results/

# Run Einstein demo
infoextract-cidoc demo --einstein

Core Features

AI-Powered Information Extraction

• LangStruct pipeline for single-pass entity and relationship extraction
• Entity Resolution with stable UUID5 identifiers and deduplication
• Relationship Resolution with broken link detection and logging
• CRM Mapping to E21 Person, E5 Event, E53 Place, E22 Object, E52 Time-Span
• DSPy optimization support for fine-tuning extraction quality
• Works with any LiteLLM-compatible model (Gemini, OpenAI, Anthropic, etc.)

Pydantic Models

• Complete CIDOC CRM v7.1.3 coverage (99 E-classes, 322 P-properties)
• Flexible UUID handling with automatic string-to-UUID conversion
• Canonical JSON schema with stable IDs and explicit cross-references
• Auto-generated from curated YAML specifications

Class Naming Convention

• Official CIDOC CRM: E1, E22, E96 (class codes)
• Python Classes: EE1_CRMEntity, EE22_HumanMadeObject, EE96_Purchase
• Pattern: E{code}_{label_without_spaces}

Markdown Renderers

• Entity Cards: Concise summaries optimized for LLM prompts
• Detailed Narratives: Rich descriptions with full context
• Tabular Summaries: Structured data presentation

NetworkX Integration

• Direct conversion from CRM entities to NetworkX graphs
• Built-in social network analysis (centrality, communities)
• Temporal network analysis for historical data

Output Formats

• Markdown (4 styles): entity cards, detailed, tabular, narrative
• Cypher: idempotent MERGE/UNWIND scripts for Neo4j/Memgraph
• NetworkX: graph objects for programmatic analysis
• GraphForge (optional): pip install infoextract-cidoc[graphforge]

Validation Framework

• Cardinality enforcement (configurable from warnings to strict)
• Type alignment validation
• Extensible validation profiles

Complete Workflow

from infoextract_cidoc.extraction import LangStructExtractor, resolve_extraction, map_to_crm_entities
from infoextract_cidoc.io.to_networkx import to_networkx_graph
from infoextract_cidoc.io.to_cypher import generate_cypher_script
from infoextract_cidoc.visualization import plot_network_graph

# 1. Extract entities via LangStruct
extractor = LangStructExtractor()
lite_result = await extractor.extract_async("""
Albert Einstein was born on March 14, 1879, in Ulm, Germany.
He developed the theory of relativity and won the Nobel Prize in 1921.
""")

# 2. Resolve and map to CRM
extraction_result = resolve_extraction(lite_result)
crm_entities, crm_relations = map_to_crm_entities(extraction_result)

# 3. Serialize as canonical JSON
json_data = [entity.model_dump(mode='json') for entity in crm_entities]

# 4. Convert to NetworkX graph for social network analysis
graph = to_networkx_graph(crm_entities)

# 5. Visualize the network
plot_network_graph(graph, title="Einstein's Life Network")

# 6. Export to Cypher for graph database persistence
cypher_script = generate_cypher_script(crm_entities)

Project Structure

src/infoextract_cidoc/
├── extraction/           # AI extraction pipeline
│   ├── lite_schema.py   # LangStruct output schema
│   ├── resolution.py    # Entity/relationship resolution
│   ├── crm_mapper.py    # CRM mapping layer
│   └── langstruct_extractor.py  # LangStructExtractor
├── models/               # Pydantic CRM models
│   ├── base.py          # CRMEntity, CRMRelation
│   └── generated/       # Auto-generated E-classes (99)
├── io/                   # Output modules
│   ├── to_markdown.py   # Markdown renderers
│   ├── to_cypher.py     # Cypher emitters
│   ├── to_networkx/     # NetworkX conversion
│   └── to_graphforge.py # GraphForge (optional)
├── validators/           # Validation framework
├── visualization/        # matplotlib/plotly plots
├── codegen/              # YAML -> Pydantic generation
└── tests/                # Test suite (77 tests)

Testing

make test           # Run all tests
make test-unit      # Unit tests only
make coverage       # With coverage report
make pre-push       # Full CI: lint + type-check + security + coverage

Documentation

• Quickstart - Getting started guide
• Contributing - Development workflow
• Changelog - Version history
• HOWTOs - Comprehensive modeling guide
• CIDOC CRM Standard - Official specification

Project Status

• Phase 1: Complete - Core CIDOC CRM implementation
• Phase 2: Complete - LangStruct extraction pipeline, validation, full CRM coverage
• Phase 3: Planned - Profile packs and additional analysis tools

Current Coverage: 99 E-classes, 322 P-properties (complete CRM 7.1.3) Test Status: 77 tests passing (100% success rate) CI/CD: GitHub Actions with uv, ruff, mypy, bandit, codecov

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• CIDOC CRM Working Group for the foundational ontology
• Pydantic team for the excellent validation framework
• Neo4j community for Cypher language inspiration

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Made with care for the cultural heritage and information extraction community