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.yamlFull Documentation — installation guides, tutorials, configuration reference, and API docs.
pip install tablassertThe 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.
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.
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.
uv sync --group dev --extra qc
uv run maturin develop --manifest-path rust/Cargo.toml
make checkSee CONTRIBUTING.md for the full development loop, quality gates, and pull request guidelines.
Skye Lane Goetz — Institute for Systems Biology
Gwênlyn Glusman — Institute for Systems Biology
Jared C. Roach — Institute for Systems Biology