v3.7 · gunicorn · Prometheus · Redis SSE · cosign · SBOM

The ontology mesh
for GraphRAG.

Generate production-ready OWL 2 ontologies + SHACL shapes from your relational schema or your logs — with materialised inference, full prov:wasDerivedFrom lineage, and a wizard that puts every candidate in front of you for review.

Open the wizard $ git clone … && pip install -e .

Currently source-install only. pip install ontomesh from PyPI lands in v3.6.

Built on open standards

What you can build

Three things you'd otherwise hand-craft for months.

Generate a full ontology from a database schema.

Point Ontomesh at a relational database. The headless pipeline introspects the schema, lifts it through OWL 2 / SHACL / JSON-LD / SKOS, runs the reasoner, and produces a materialised artifact bundle with full prov:wasDerivedFrom lineage.

{% if bench %} {{ bench.db_size_human }} DB · {{ bench.elapsed_human }} · {{ bench.owl_classes }} classes, {{ bench.owl_properties_human }} properties {% else %} OWL 2 · SHACL · JSON-LD · SKOS {% endif %}

Validate with SHACL. Reason with Datalog.

Shapes catch data-quality issues before they hit production. Datalog rules derive new facts with full prov:wasDerivedFrom lineage. Every reasoner run emits a materialisation report you can diff against the last release.

{% if bench %}{{ bench.shacl_shapes }} SHACL shapes {% else %}SHACL 1.1{% endif %} · SWRL · Datalog (Rulewerk)

Mine candidates from raw logs.

Log Discovery (phases L4–L13) mines templates with online clustering, surfaces candidate entities / events / relationships from co-occurrence (PMI), fits per-service HMMs for anomalies, and gates causal edges with Granger / transfer-entropy. You review one candidate at a time in the wizard.

L4 templates · L8 regimes · L11 causal DAG · L13 rate anomalies

How it works

From raw signal to graph-grounded RAG, in three steps.

  1. 1

    Discover

    Point Ontomesh at logs or a database schema. The discovery pipeline (phases L1–L13) surfaces entity / event / relationship candidates with confidence, provenance, and a one-click accept.

  2. 2

    Model & validate

    Add SHACL shapes and SWRL / Datalog rules. The reasoner materialises derived triples with lineage; the competency-question runner gives you regression coverage as you iterate.

  3. 3

    Generate & ship

    One click emits an artifact bundle — OWL Turtle, SHACL, JSON-LD, pydantic / TypeScript bindings — plus a hybrid retriever ready to drop into your RAG stack.

Get started

One command, a full ontology bundle.

# 1. Install (PyPI publish lands in v3.6 — for now, source install)
git clone https://github.com/synaptixs/ontomesh.git
cd ontology && pip install -e .

# 2. Generate the full OWL / SHACL / JSON-LD bundle from a SQLite DB
ontomesh --db db/demo.db --out ./output

# 3. Or launch the interactive wizard at http://localhost:5051
ontomesh-wizard --port 5051

# 4. Mine candidates from raw logs (phases L4–L13)
ontomesh --phase log --db db/demo.db --out ./output

Prefer to point and click? Open the wizard →

Stop hand-crafting ontologies for your RAG stack.

Open the wizard, load a sample domain (telecom, healthcare, e-commerce), and watch a real ontology + retriever fall out the other end in under a minute.

Open the wizard →