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Tutorial: Your First Knowledge Graph

By the end of this tutorial you will have built a KGX-compliant knowledge graph from a CSV of gene-disease associations: nodes and edges with standardized CURIEs, biolink categories, provenance, and statistical annotations, ready for NCATS Translator. You'll learn the complete workflow: creating configurations, running Tablassert, and examining the output.

The input files ship under docs/examples/ and are validated against the live schema by the test suite; the configurations below reproduce them exactly.

Time: 5-10 minutes

Prerequisites

  • Tablassert installed (see Installation)
  • Required database: fullmap
  • Basic familiarity with YAML

The Data

The input is a CSV of gene-disease associations, shipped at docs/examples/tutorial-data.csv:

gene_symbol,disease_name,p_value,sample_size
TP53,lung cancer,0.001,450
BRCA1,breast cancer,0.0001,1200
EGFR,colorectal cancer,0.005,680
KRAS,pancreatic cancer,0.002,320

Goal: Transform this into KGX-compliant nodes and edges.

Step 1: Create the Data File

If you're following along outside the repository, save the CSV from The Data above as tutorial-data.csv.

Step 2: Create Table Configuration

Create tutorial-table.yaml. This config is shipped, test-validated, at docs/examples/tutorial-table.yaml (checked against the live schema by the test suite); the listing below matches it exactly:

template:
  source:
    kind: text
    local: ./docs/examples/tutorial-data.csv
    url:
      - https://example.com/data.csv
    row_slice:
      - 1
      - auto
    delimiter: ","
  statement:
    subject:
      method: column
      encoding: A
      prioritize:
        - Gene
    predicate: associated_with
    object:
      method: column
      encoding: B
      prioritize:
        - Disease
  provenance:
    repo: PMID
    publication: "12345678"
  annotations:
    - annotation: p_value
      method: column
      encoding: C
    - annotation: study_size
      method: column
      encoding: D

Paths

The shipped fixture reads the CSV from docs/examples/tutorial-data.csv (repo-root-relative). If you saved your own tutorial-data.csv in Step 1, set source.local: ./tutorial-data.csv instead.

What this does: - source: Reads the CSV, skipping the header row (row_slice starts at 1) - statement: Creates edges where genes (subject) are associated_with diseases (object) - subject/object: Uses column method to read from columns A (gene symbol) and B (disease name) - prioritize: Tells entity resolution to prefer Gene/Disease categories - annotations: Adds p-value (column C) as an edge attribute and study size (column D) as metadata on the inlined supporting study

Step 3: Create Graph Configuration

Create tutorial-graph.yaml. The shipped, test-validated version lives at docs/examples/tutorial-graph.yaml; the listing below matches it exactly:

name: TUTORIAL_KG
version: 1.0.0
tables:
  - ./docs/examples/tutorial-table.yaml
fullmap: /path/to/fullmap
rig:
  source_info:
    infores_id: infores:tutorial-kg
    name: Tutorial tabular source
    description: Example source data used by the Tablassert tutorial graph.
    terms_of_use_info:
      license_name: CC0 1.0 Universal
      license_url: https://creativecommons.org/publicdomain/zero/1.0/
    data_access_locations:
      - Tutorial data - https://example.com/data.csv
    source_status: unknown
  ingest_info:
    utility: Demonstrates how Tablassert turns configured tabular records into Translator-ready KGX.
    scope: Gene-disease associations from the tutorial table, one edge per configured row.
  provenance_info:
    contributions:
      - "Tutorial author: config author, data modeling"
  artifact_base_url: https://example.com/tutorial-kg
  artifact_base_path: ./tutorial-output

Important: Replace fullmap with the path to your fullmap redb (and adjust tables if your table config lives elsewhere). The rig: section is required; see the Graph configuration reference for every field.

What this does: - name/version: Output files will be TUTORIAL_KG_1.0.0.nodes.ndjson, TUTORIAL_KG_1.0.0.edges.ndjson, and TUTORIAL_KG_1.0.0.RIG.yaml (written into rig.artifact_base_path, here ./tutorial-output/) - rig: Resource Ingest Guide metadata (source info, utility/scope, provenance, artifact bases) emitted as the .RIG.yaml - tables: List of table configurations to process - fullmap: Path to the fullmap redb file (or base directory) for entity resolution

Step 4: Run Tablassert

tablassert build-kg tutorial-graph.yaml

To also run the quality-control audit stage, add --qc; add --log for verbose per-section logging when the [log] extra is installed:

tablassert build-kg tutorial-graph.yaml --qc --log

What happens: 1. Loads graph configuration 2. For each table config: - Reads the source file from disk - Applies row slicing - Resolves entities (genes and diseases) - Validates with the QC pipeline when --qc is passed (exact → fuzzy → abbreviation → SapBERT) - Creates subgraph parquet file 3. Aggregates all subgraphs 4. Exports NDJSON files and the RIG

Step 5: Examine Output

Nodes file:

head -n 3 tutorial-output/TUTORIAL_KG_1.0.0.nodes.ndjson

Example output: 

{"id":"HGNC:11998","name":"TP53","category":["biolink:Gene"],"taxon":"NCBITaxon:9606"}
{"id":"MONDO:0008903","name":"lung cancer","category":["biolink:Disease"]}
{"id":"HGNC:1100","name":"BRCA1","category":["biolink:Gene"],"taxon":"NCBITaxon:9606"}

Edges file:

head -n 2 tutorial-output/TUTORIAL_KG_1.0.0.edges.ndjson

Example output (p-values are emitted as controlled scientific-notation strings and the derived significance band as a bare enum token; the study size is Study.study_size metadata on the inlined supporting study, anchored to the source row by the row:<N> StudyResult): 

{"id":"2cfea591-0f8f-33af-a7df-03da531d3359","subject":"HGNC:11998","predicate":"biolink:associated_with","object":"MONDO:0008903","p_value":"1.0000e-03","statistical_significance_qualifier":"strongly_significant","has_supporting_studies":{"PMID:12345678":{"id":"PMID:12345678","name":"tutorial-data.csv","study_size":450,"has_study_results":[{"id":"row:2"}]}},"publications":["PMID:12345678"]}
{"id":"7b1c9d02-5e8a-4f3b-9c1d-2a6e8f0b4d7c","subject":"HGNC:1100","predicate":"biolink:associated_with","object":"MONDO:0005041","p_value":"1.0000e-04","statistical_significance_qualifier":"very_strongly_significant","has_supporting_studies":{"PMID:12345678":{"id":"PMID:12345678","name":"tutorial-data.csv","study_size":1200,"has_study_results":[{"id":"row:3"}]}},"publications":["PMID:12345678"]}

RIG file:

cat tutorial-output/TUTORIAL_KG_1.0.0.RIG.yaml

The Resource Ingest Guide records the graph's source metadata and terms of use (rig.source_info), ingest utility and scope (rig.ingest_info), provenance (rig.provenance_info), the generated artifact locations, and a summary of the emitted node and edge types, all validated in memory before the file is written, for NCATS Translator registration.

Understanding the Transformation

Entity resolution maps text to CURIEs ("TP53" → HGNC:11998 Gene, "lung cancer" → MONDO:0008903 Disease); when --qc is passed, the QC pipeline validates each mapping across four stages (exact → fuzzy → abbreviation → SapBERT). The output is KGX-compliant nodes and edges with standardized CURIEs, Biolink categories and predicates, provenance, and edge annotations.

Next Steps