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Tablassert is a highly performant declarative knowledge graph backend designed to extract knowledge assertions from tabular data while exporting NCATS Translator-compliant Knowledge Graph Exchange (KGX) NDJSON.

Project description

Tablassert

PyPI Python License Docs

Extract knowledge assertions from tabular data into NCATS Translator-compliant KGX NDJSON — declaratively, with entity resolution and quality control built in.

pip install tablassert
tablassert build-knowledge-graph config.yaml

Full Documentation — installation guides, tutorials, configuration reference, and API docs.

Installation

pip install tablassert

All dependencies (ML, web, Excel support) are included in the base install. An optional extra is available for CPU compatibility:

pip install "tablassert[rtcompat]"  # Polars build for CPUs without required instructions
Docker
docker pull ghcr.io/skyeav/tablassert:latest

docker run --rm \
  -v /path/to/config:/data \
  -v /path/to/datassert:/datassert \
  ghcr.io/skyeav/tablassert:latest \
  build-knowledge-graph /data/graph-config.yaml

Key Features

  • Declarative Configuration — YAML-based, no code required
  • Entity Resolution — Maps text to biological entities (genes, diseases, chemicals)
  • Quality Control — Three-stage validation (exact → fuzzy → BERT embeddings)
  • KGX Compliance — NCATS Translator-compatible NDJSON output
  • Performance — Lazy evaluation pipelines with Polars and DuckDB-accelerated entity resolution

Contributing

See CONTRIBUTING.md for development setup, code style, and pull request guidelines.

License

Apache License 2.0

Contributors

Skye Lane Goetz — Institute for Systems Biology, CalPoly SLO

Gwênlyn Glusman — Institute for Systems Biology

Jared C. Roach — Institute for Systems Biology

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