glazing 0.1.1

Unified data models and interfaces for syntactic and semantic frame ontologies

Glazing

Unified data models and interfaces for syntactic and semantic frame ontologies.

Features

• 🚀 One-command setup: glazing init downloads and prepares all datasets
• 📦 Type-safe models: Pydantic v2 validation for all data structures
• 🔍 Unified search: Query across all datasets with consistent API
• 🔗 Cross-references: Automatic mapping between resources
• 💾 Efficient storage: JSON Lines format with streaming support
• 🐍 Modern Python: Full type hints, Python 3.13+ support

Installation

pip install glazing

Quick Start

Initialize all datasets (one-time setup, ~54MB download):

glazing init

Then start using the data:

from glazing.search import UnifiedSearch

# Automatically uses default data directory after 'glazing init'
search = UnifiedSearch()
results = search.search("give")

for result in results[:5]:
    print(f"{result.dataset}: {result.name} - {result.description}")

CLI Usage

Search across datasets:

# Search all datasets
glazing search query "abandon"

# Search specific dataset
glazing search query "run" --dataset verbnet

# Find cross-references
glazing search cross-ref --source propbank --id "give.01" --target verbnet

Python API

Load and work with individual datasets:

from glazing.framenet.loader import FrameNetLoader
from glazing.verbnet.loader import VerbNetLoader

# Loaders automatically use default paths and load data after 'glazing init'
fn_loader = FrameNetLoader()  # Data is already loaded
frames = fn_loader.frames

vn_loader = VerbNetLoader()  # Data is already loaded
verb_classes = list(vn_loader.classes.values())

Cross-reference resolution:

from glazing.references.extractor import ReferenceExtractor
from glazing.verbnet.loader import VerbNetLoader
from glazing.propbank.loader import PropBankLoader

# Load datasets
vn_loader = VerbNetLoader()
pb_loader = PropBankLoader()

# Extract references
extractor = ReferenceExtractor()
extractor.extract_verbnet_references(list(vn_loader.classes.values()))
extractor.extract_propbank_references(list(pb_loader.framesets.values()))

# Access PropBank cross-references
if "give.01" in extractor.propbank_refs:
    refs = extractor.propbank_refs["give.01"]
    vn_classes = refs.get_verbnet_classes()
    print(f"VerbNet classes for give.01: {vn_classes}")

Supported Datasets

• FrameNet 1.7: Semantic frames and frame elements
• PropBank 3.4: Predicate-argument structures
• VerbNet 3.4: Verb classes with thematic roles
• WordNet 3.1: Synsets and lexical relations

Documentation

Full documentation available at https://glazing.readthedocs.io.

• Installation Guide
• Quick Start Tutorial
• API Reference
• CLI Documentation

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

# Development setup
git clone https://github.com/aaronstevenwhite/glazing
cd glazing
pip install -e ".[dev]"

Citation

If you use Glazing in your research, please cite:

@software{glazing2025,
  author = {White, Aaron Steven},
  title = {Glazing: Unified Data Models and Interfaces for Syntactic and Semantic Frame Ontologies},
  year = {2025},
  url = {https://github.com/aaronstevenwhite/glazing},
  doi = {10.5281/zenodo.17185626}
}

License

This package is licensed under an MIT License. See LICENSE file for details.

Links

• GitHub Repository
• PyPI Package
• Documentation
• Issue Tracker

Acknowledgments

This project was funded by a National Science Foundation (BCS-2040831) and builds upon the foundational work of the FrameNet, PropBank, VerbNet, and WordNet teams.