Tablassert

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

pip install tablassert
tablassert build-kg config.yaml

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

Installation

pip install tablassert

The base install builds knowledge graphs from CSV/TSV/Excel sources. Optional extras (rt, qc, agent) add CPU-compatible Polars, the three-stage QC audit, and the autonomous agent — see the Installation guide for the full matrix. QC is opt-in at build time (build-kg --qc); see the CLI Reference for the complete flag reference.

Quick Demo

from pathlib import Path
from tablassert.lib import resolve_many

results = resolve_many(col="gene", entities=["TP53", "BRCA1"], fullmap=Path("/path/to/fullmap"), taxon="9606")
# [{"original_gene": "TP53", "gene": "HGNC:11998", "gene_name": "TP53", ...}, ...]

Point resolve_many() at a fullmap database to resolve any iterable of entity strings to CURIEs — no LazyFrame setup or NLP preprocessing required. See the Batch Resolution API for the full reference; for YAML-configured pipeline builds use tablassert build-kg config.yaml.

Key Features

Declarative YAML configs, built-in entity resolution, optional three-stage QC, and KGX-compliant NDJSON output — with lazy Polars pipelines over an embedded redb resolution database. See the documentation for the full feature overview and use-case gallery.

Developing

uv sync --group dev --extra qc
uv run maturin develop --manifest-path rust/Cargo.toml
make check

See CONTRIBUTING.md for the full development loop, quality gates, and pull request guidelines.

License

Apache License 2.0

Contributors

Skye Lane Goetz — Institute for Systems Biology

Gwênlyn Glusman — Institute for Systems Biology

Jared C. Roach — Institute for Systems Biology

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tablassert 8.1.0

Tablassert

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