Open Ontologies is a Rust MCP server and desktop Studio for AI-native ontology engineering. It exposes 70+ tools that let Claude build, validate, query, diff, lint, version, reason over, align, plan, certify, and govern RDF/OWL ontologies using an in-memory Oxigraph triple store — with a full three-layer Dynamics → Causal → Planner architecture, a marketplace of 32 standard ontologies, clinical crosswalks, semantic embeddings, and a full lineage audit trail.

The Studio wraps the engine in a visual desktop environment: virtualized ontology tree with hierarchy lines, breadcrumb navigation, and connection explorer; AI chat panel with /build (IES-level deep) and /sketch (quick prototype) commands; Protégé-style property inspector; and lineage viewer.

No JVM. No Protégé.

What's New (three-layer architecture + 13 new primitives)

The full Dynamics → Causal → Planner stack plus 13 new primitives. Every piece holds the MCP-native convention: the server provides validation and scaffolding, the connected LLM (Claude over MCP) does the intelligence. No internal LLM clients, no API keys, no provider abstractions.

Three-layer architecture

Layer	What it ships
Dynamics	`ActionSchema` + 4 MCP tools: `onto_action_register` / `_applicable` / `_apply` / `_list`. Concurrent atomic ticks, static causal laws (invariants), default-value laws, ramification via OWL-RL closure, non-deterministic outcomes with reproducible seed.
Causal	`onto_certify_action` with optional PyWhy backdoor identification (opt-in via `causal-pywhy` feature). Structural-proxy default + do-calculus opt-in + graceful fallback.
Planner	`onto_plan_compile_pddl` + `onto_plan_classical` (Fast Downward subprocess) + `onto_plan_validate` (sandbox-simulate). Solver stays client-side; server compiles + validates.

13 new primitives

onto_owl_shacl_coevolve_check + onto_owl_shacl_coevolve_incremental — SHACL validation against the OWL-RL closure, with dependency-graph routing so only shapes touching changed IRIs revalidate.
onto_segment_retrieve — TBox-slice retrieval for ontology-grounded RAG.
onto_extract_scaffold + onto_extract_validate — schema-guided structured extraction with typed datatype validation + conformance scoring.
onto_cq_run + onto_verify_cq + onto_cq_verdicts_list — competency-question runner with pitfall hints + LLM-judgement loop.
onto_classify_el — OWL-EL classification (transitive subsumption table, trivial pairs excluded).
onto_eval_alignment — P/R/F1 over reference + computed alignment sets.
onto_shape_combinatorics + onto_shape_induce — property-combination lattice + data-driven SHACL shape induction with support × confidence ranking.
borderline_partition + borderline_record_verdict — generalised two-threshold review loop for any candidate set.
onto_align_fuzzy — embedding-free fuzzy-logic adjudication with 10-rule Mamdani inference; HNSW is demoted to a candidate generator.
onto_align_flora — end-to-end alignment pipeline pairing the signal extractor to the fuzzy adjudicator.
onto_policy_register + onto_policy_list + onto_policy_check — authorisation gate that composes with onto_certify_action (Causal = risk; policy = authorisation).
eval_rag + eval_rag_mmrag — Hit@k / MRR / faithfulness / token-Jaccard / ROUGE-1 scoring for retriever pipelines, with a dataset adapter.
graph_projection_lossy_check — the auditor that pairs with onto_segment_retrieve.

Validating end-to-end

cargo run --example three_layer_pipeline

Walks Dynamics register → PDDL compile → Fast-Downward-shaped sas_plan parse → orchestrator-side IRI bind → sandbox validate → CIVeX certify → apply with OWL-RL ramification → final state inspection. Every layer through its public API, no external dependencies (Python, DoWhy, Fast Downward) required.

Zero new external Rust dependencies; everything optional gates behind Cargo features. Full test suite (160+ tests) green on default build; cargo clippy --lib --tests --examples -- -D warnings clean across both default and causal-pywhy configurations.

Quick Start (MCP / CLI)

Install

Pre-built binaries:

# macOS (Apple Silicon)
curl -LO https://github.com/fabio-rovai/open-ontologies/releases/latest/download/open-ontologies-aarch64-apple-darwin
chmod +x open-ontologies-aarch64-apple-darwin && mv open-ontologies-aarch64-apple-darwin /usr/local/bin/open-ontologies

# macOS (Intel)
curl -LO https://github.com/fabio-rovai/open-ontologies/releases/latest/download/open-ontologies-x86_64-apple-darwin
chmod +x open-ontologies-x86_64-apple-darwin && mv open-ontologies-x86_64-apple-darwin /usr/local/bin/open-ontologies

# Linux (x86_64)
curl -LO https://github.com/fabio-rovai/open-ontologies/releases/latest/download/open-ontologies-x86_64-unknown-linux-gnu
chmod +x open-ontologies-x86_64-unknown-linux-gnu && mv open-ontologies-x86_64-unknown-linux-gnu /usr/local/bin/open-ontologies

Docker:

docker pull ghcr.io/fabio-rovai/open-ontologies:latest
docker run -i ghcr.io/fabio-rovai/open-ontologies serve

serve starts an MCP server that speaks JSON-RPC over stdin/stdout — it is not an interactive CLI, so on launch it will appear to "hang" while it waits for an MCP client to connect. That is expected. To try the tools directly from a terminal instead, use the CLI subcommands (e.g. open-ontologies validate <file.ttl>); to use it with an LLM, wire it into an MCP client as shown under Connect to your MCP client.

From source (Rust 1.85+):

git clone https://github.com/fabio-rovai/open-ontologies.git
cd open-ontologies && cargo build --release
./target/release/open-ontologies init

For native Windows builds, see docs/windows.md.

Connect to your MCP client

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "open-ontologies": {
      "command": "/path/to/open-ontologies/target/release/open-ontologies",
      "args": ["serve"]
    }
  }
}

Restart Claude Code. The onto_* tools are now available.

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "open-ontologies": {
      "command": "/path/to/open-ontologies/target/release/open-ontologies",
      "args": ["serve"]
    }
  }
}

Add to .cursor/mcp.json or equivalent:

{
  "mcpServers": {
    "open-ontologies": {
      "command": "/path/to/open-ontologies/target/release/open-ontologies",
      "args": ["serve"]
    }
  }
}

{
  "mcpServers": {
    "open-ontologies": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "ghcr.io/fabio-rovai/open-ontologies", "serve"]
    }
  }
}

Build your first ontology

Build me a Pizza ontology following the Manchester University tutorial.
Include all 49 toppings, 24 named pizzas, spiciness value partition,
and defined classes (VegetarianPizza, MeatyPizza, SpicyPizza).
Validate it, load it, and show me the stats.

Claude generates Turtle, then runs the full pipeline automatically:

onto_validate → onto_load → onto_stats → onto_reason → onto_stats → onto_lint → onto_enforce → onto_query → onto_save → onto_version

Every build includes OWL reasoning (materializes inferred triples), design pattern enforcement, and automatic versioning.

Studio (Desktop App)

The Studio is a native desktop application that wraps the same engine in a visual environment — no browser, no server to manage. It runs entirely on your machine: the engine sidecar handles RDF/OWL operations while the UI renders the graph in real time.

Think of it as Protege meets an AI copilot. Type "build ontology about cats" and watch a 1,400-class ontology appear in the tree — classes, properties, individuals, and axioms built automatically across 13 pipeline steps. Click any node to inspect its triples, trace connections via clickable pills, and follow every change through the lineage panel.

Why virtualized tree (not 3D graph)

Prior to v0.1.12, the Studio used a D3.js horizontal tree and a 3D force-directed graph (Three.js / WebGL). Both worked for small ontologies (~100 classes) but became unusable at IES-level depth: the D3 tree couldn't handle 500+ nodes without layout thrashing, and the 3D graph froze the WebKit webview above 1,000 nodes.

The v2 deep builder changed the equation — a single /build command now produces 1,400+ classes. We replaced both views with a virtualized DOM tree: only visible rows exist in the DOM (constant memory regardless of ontology size), with hierarchy connector lines, type-filtered legend, search, breadcrumb navigation, and a connections panel. This handles the full IES Common (511 classes) and deep-built ontologies (1,400+ classes) without lag.

How it works

The Studio launches three processes that communicate locally:

Tauri 2 shell — native window (macOS/Linux/Windows) with a WebKit webview
Engine sidecar — the same Rust binary, running as an HTTP MCP server on localhost:8080
Agent sidecar — Node.js process running Claude via the Agent SDK, connected to the engine over MCP

When you type in the chat panel, your message goes to the Agent sidecar, which sends it to Claude. Claude decides which onto_* tools to call, the engine executes them, and the UI refreshes the graph. The entire loop — prompt to visual update — takes seconds.

Install and run

Prerequisites: Rust + Cargo · Node.js 18+

# 1. Build the engine binary (from repo root)
cargo build --release

# 2. Install JS dependencies
cd studio && npm install

# 3. Run
PATH=/opt/homebrew/bin:~/.cargo/bin:$PATH npm run tauri dev

The first launch compiles the Tauri shell (~2 min). Subsequent launches start in seconds.

Features

Feature	Description
Virtualized Tree	Ontology explorer that handles 1,500+ classes without lag. Hierarchy connector lines, collapsible branches, type-filtered legend (Class/Property/Individual), search with auto-expand, breadcrumb path navigation, and a connections panel showing domain/range relationships as clickable pills. Only visible rows are in the DOM — constant memory regardless of ontology size.
AI Agent Chat	Natural language ontology engineering via Claude Opus 4.8 + Agent SDK. Two build modes: `/build` runs a 13-step pipeline producing IES-level ontologies (500-1,500+ classes, 100-200+ properties), `/sketch` runs 3 steps for quick prototyping (~80 classes). Each tool call is shown in real time.
Property Inspector	Protege-style inline triple editor. Click any node to see its `rdfs:subClassOf`, `rdfs:label`, `rdfs:domain`, `rdfs:range` and all other triples. Edit in place, hover to delete, `+ Add` for new triples. Changes are immediately reflected in the graph.
Lineage Panel	Full audit trail from SQLite: every plan, apply, enforce, drift, monitor, and align event, grouped by session with timestamps. See exactly what Claude did and in what order.
Named Save	`⌘S` to save as `~/.open-ontologies/<name>.ttl`. Auto-saves to `studio-live.ttl` after every mutation so you never lose work.

Keyboard shortcuts

Shortcut	Action
`⌘J`	Toggle AI chat panel
`⌘I`	Toggle property inspector
`⌘S`	Save ontology
`F`	Fit graph to viewport (tree view)
`R`	Reset zoom (tree view)
`Esc`	Deselect node
`Shift+click`	Collapse/expand branch (tree view)
`Scroll`	Zoom in/out
`Click + drag`	Pan

Benchmarks

How to read these numbers. Unless stated, LLM results are single-run (not averaged over seeds) and use Claude Opus 4.8. Several benchmark ontologies (Pizza, FOAF, Schema.org, OWL-Time) are widely published and may appear in an LLM's pretraining data, so a bare-LLM score is a contamination-inclusive baseline, not a clean measure of reasoning — the contribution is the delta the MCP tools add on top of that baseline, and whether that delta reproduces across models. To check exactly that, the repo ships a cross-model ablation driving the same tasks with a local Qwen3-Coder-30B as well as Claude — see benchmark/ontoaxiom/. If the tool-augmented gain holds on a second, open model, the gain is a property of the tooling, not of one vendor's model.

OntoAxiom — LLM Axiom Identification

OntoAxiom tests axiom identification across 9 ontologies and 3,042 ground truth axioms.

All conditions below are scored by a single evaluator (benchmark/ontoaxiom/score_all_conditions.py) with one shared normalizer, and both averages are reported, because the original scripts disagreed on all of that. macro = mean of per-(ontology, axiom) F1; micro = F1 over pooled TP/FP/FN.

Approach	Input	macro F1	micro F1
o1 (paper's best)	Name lists	—	0.197
Bare Claude Opus	Name lists	0.451	0.397
Bare Qwen3-Coder-30B	Name lists	0.223	0.176
Claude Opus, raw OWL file	Full Turtle	0.768	0.686
Qwen3-Coder-30B, raw OWL file	Full Turtle	0.673	0.667
MCP extraction	Full OWL	0.713	0.717

The paper's "raw OWL hurts" result is a scoring artifact. OntoAxiom reports that an LLM given the full OWL file (F1 = 0.323) does worse than one given only class/property name lists (0.431). Those two numbers came from scripts that disagreed on three axes: the name-list scorer splits camelCase and the raw-OWL scorer only lowercases; the first reports a macro mean and the second a micro F1; and they flip pair order on different axiom types. Every one of those differences penalizes the raw-OWL condition, because that is the one where the model reads real Turtle and therefore answers in QNames (foaf:Person) and rdfs:label text ("personal mailbox" for mbox) rather than bare local names. 0.431 and 0.323 were never the same statistic.

Rescoring the same stored predictions under one evaluator flips the sign on both models and under both averages: Claude 0.451 → 0.768 macro (0.397 → 0.686 micro), Qwen 0.246 → 0.673 macro, winning 33/43 and 33/38 cells respectively. score_condition_d.py --legacy reproduces the broken 0.323 exactly, so the bug is demonstrated rather than asserted. The correction moves 0 of 5,083 name-list pairs, so it cannot flatter the baseline, and it still under-credits raw OWL: 51.8% of Claude's pairs are label text no normalizer here can match. Full analysis and reproduction: benchmark/ontoaxiom/ONTOAXIOM_SHOWDOWN.md.

Corrected, reading the ontology and SPARQL-extracting it are at parity — raw OWL wins macro (0.768 vs 0.713), extraction wins micro (0.717 vs 0.686). So the tools' edge is auditability, not F1: every MCP pair traces to a query against real triples, whereas an LLM reading a file can still hallucinate a plausible pair and no F1 score will say which.

Pizza Ontology — Manchester Tutorial

One sentence input: "Build a Pizza ontology following the Manchester tutorial specification."

Metric	Reference (Protégé, ~4 hours)	AI-Generated (~5 min)	Coverage
Classes	99	95	96%
Properties	8	8	100%
Toppings	49	49	100%
Named Pizzas	24	24	100%

`/sketch` vs `/build` — Two Build Modes

The Studio provides two build commands for different use cases. Both take the same input — "build ontology about cats" — but produce very different results:

Metric	`/sketch` (3 steps, ~2 min)	`/build` (13 steps, ~15 min)	IES Common (reference)
Classes	95	1,433	511
Object properties	15	218	162
Datatype properties	5	101	44
Individuals	3	358	21
Disjoints	6	60+	—
Max hierarchy depth	5	11	8
Build time	~2 min	~15 min	— (hand-built)

/sketch runs 3 steps: classes + properties in one Turtle block, axioms + individuals, then save. Good for quick domain exploration or demo prototyping. Produces a complete ontology with hierarchy, properties, and individuals — but at a fraction of the depth.

/build runs a 13-step pipeline within a single persistent Claude session: foundation classes → per-branch deepening (4 passes) → gap filling → object properties (2 batches) → datatype properties → disjoints → individuals → reason → save. Each step focuses on one aspect of the ontology, staying within output token limits while building on the previous step's context. The result exceeds IES Common on every metric.

/sketch is comparable to the Pizza benchmark (95 classes, 8 properties). /build produces IES-level ontologies — deep enough for production use.

Mushroom Classification — OWL Reasoning vs Expert Labels

Dataset: UCI Mushroom Dataset — 8,124 specimens classified by mycology experts.

Metric	Result
Accuracy	98.33%
Recall (poisonous)	100% — zero toxic mushrooms missed
False negatives	0
Classification rules	6 OWL axioms

Ontology Marketplace — 32 Standard Ontologies

All 32 marketplace ontologies fetched, owl:imports resolved, loaded, and reasoned over with both RDFS and OWL-RL profiles:

Ontology	Classes	Properties	Triples	+ RDFS	+ OWL-RL	Fetch	RDFS	OWL-RL
OWL 2	32	4	537	+230	+230	681ms	6ms	3ms
RDF Schema	6	0	87	+35	+35	522ms	2ms	1ms
RDF Concepts	7	0	127	+31	+31	545ms	2ms	2ms
BFO (ISO 21838)	35	0	1,221	+186	+186	1,141ms	5ms	4ms
DOLCE/DUL	93	118	1,917	+666	+692	2,208ms	13ms	12ms
Schema.org	1,009	0	17,823	+4,031	+13,670	558ms	57ms	117ms
FOAF	28	60	631	+4	+31	940ms	3ms	2ms
SKOS	5	18	252	+55	+55	218ms	2ms	1ms
Dublin Core Elements	0	0	107	+0	+0	371ms	2ms	1ms
Dublin Core Terms	22	0	700	+256	+261	259ms	4ms	3ms
DCAT	58	89	2,841	+223	+254	975ms	15ms	11ms
VoID	8	8	216	+0	+0	531ms	2ms	2ms
DOAP	17	0	741	+0	+0	727ms	2ms	2ms
PROV-O	39	50	1,146	+202	+203	472ms	5ms	4ms
OWL-Time	23	58	1,296	+165	+165	256ms	5ms	4ms
W3C Organization	22	33	748	+9	+21	639ms	4ms	3ms
SSN	35	38	1,815	+84	+84	519ms	6ms	4ms
SOSA	29	23	396	+0	+0	1,264ms	3ms	2ms
GeoSPARQL	12	54	796	+4	+12	733ms	3ms	3ms
LOCN	2	0	206	+0	+0	1,031ms	2ms	1ms
SHACL	40	0	1,128	+268	+268	662ms	5ms	3ms
vCard	75	84	882	+0	+46	854ms	3ms	3ms
ODRL	71	50	2,157	+73	+76	798ms	6ms	5ms
Creative Commons	6	0	115	+0	+49	184ms	1ms	1ms
SIOC	14	83	615	+0	+2	863ms	3ms	2ms
ADMS	4	13	151	+0	+0	747ms	3ms	1ms
GoodRelations	98	102	1,834	+15	+42	2,299ms	6ms	6ms
FIBO (metadata)	0	0	45	+0	+0	1,524ms	3ms	1ms
QUDT	73	175	2,434	+1,574	+1,581	2,934ms	14ms	9ms
Total	1,863	1,060	42,964	+8,111	+17,994	—	—	—

32/32 ontologies loaded, imports resolved, and reasoned. RDFS adds 18% more triples. OWL-RL adds 41% — transitive/symmetric/inverse properties and equivalentClass expansion discover significantly more implicit knowledge. Schema.org jumps from +4,031 (RDFS) to +13,670 (OWL-RL) inferred triples in 117ms.

Reasoning Performance — vs HermiT

LUBM Scaling (load + reason cycle)

Axioms	Open Ontologies	HermiT	Speedup
1,000	15ms	112ms	7.5×
5,000	14ms	410ms	29×
10,000	14ms	1,200ms	86×
50,000	15ms	24,490ms	1,633×

Full benchmark writeup: docs/benchmarks.md

OAEI Ontology Alignment — Anatomy Track

OAEI is the standard benchmark for ontology alignment systems. The Anatomy track aligns 2,737 mouse anatomy classes to 3,304 human anatomy classes against 1,516 reference mappings.

System	Precision	Recall	F1
AML	0.950	0.922	0.936
BERTMap	0.940	0.910	0.924
LogMap	0.930	0.890	0.912
OLaLa	0.900	0.880	0.890
Open Ontologies	0.963	0.733	0.832

Open Ontologies uses 7 weighted signals (label similarity, property/parent/instance/restriction/neighbourhood overlap, embedding similarity), stable 1-to-1 matching, and a label penalty when no structural evidence is available. No external background knowledge (UMLS, BioPortal) is used. See issues #8, #9, #10 for planned improvements.

IES Support

IES (Information Exchange Standard) is the UK National Digital Twin Programme's core ontology framework. It uses a 4D extensionalist (BORO) approach for modelling entities, events, states, and relationships. Open Ontologies supports the full IES stack — all three layers, SHACL shapes, and example datasets from the IES-Org GitHub repositories.

The IES Layers

The marketplace includes all three tiers of the IES framework:

onto_marketplace install ies-top     # ToLO — BORO foundations (~22 classes)
onto_marketplace install ies-core    # Core — persons, states, events (~131 classes)
onto_marketplace install ies         # Common — full ontology (511 classes, 206 properties)

Benchmark

Metric	IES Common
Classes	511
Object properties	162
Datatype properties	44
Total properties	206
Triples loaded	4,041
+ RDFS inferred	+3,094 (+77%)
Fetch time	911ms
RDFS reasoning	63ms
Lint issues	0

IES is the second-largest ontology in the marketplace by class count (after Schema.org). RDFS reasoning produces the richest inference gain of any non-general ontology — State, ClassOfEntity, and Event subclasses all generating deep transitive chains.

Example Data

Load IES example datasets directly from the official repositories:

onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/examples/sample-data/event-participation.ttl
onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/examples/sample-data/hospital.ttl
onto_pull https://raw.githubusercontent.com/telicent-oss/ies-examples/main/additional_examples/ship_movement.ttl

SHACL Validation

onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/specification/ies-common.shacl
onto_shacl

Data Mapping: EPC → IES

The repo includes a sample of real UK Energy Performance Certificates (benchmark/epc/epc-sample.csv) with a mapping config that transforms tabular EPC data into IES-shaped RDF:

onto_load benchmark/generated/ies-building-extension.ttl
onto_ingest benchmark/epc/epc-sample.csv --mapping benchmark/epc/epc-ies-mapping.json
onto_reason --profile rdfs

This mirrors NDTP's actual pipeline: CSV → IES RDF → validate → reason → query.

IES Building Extension — Comparison with NDTP/IRIS

The repo includes an IES Building Extension built from the UK EPC data schema and building science fundamentals, using IES 4D patterns. It was built independently — without reference to any existing implementation — then compared against the NDTP/IRIS production building ontology used in government data pipelines.

Metric	NDTP/IRIS (hand-built)	Open Ontologies (AI-built)
Schema
Classes	244	525
Properties	34	104
Triples (raw)	1,346	3,229
Lint issues	2	0
Reasoning
RDFS inferred	621	662
Triples after RDFS	1,967	3,891
Max hierarchy depth	7	10
Avg hierarchy depth	2.89	2.02
EPC Coverage
EPC columns covered	18/36 (50%)	36/36 (100%)
4D Pattern
Complete triads (Entity+State+ClassOf)	14	129
Enumerated individuals	2	214

Built blind from the 105-column EPC schema, SAP methodology, and BORO 4D extensionalism — zero reference to the IRIS implementation. The two ontologies make different trade-offs: IRIS is more tightly curated with higher average hierarchy depth (2.89 vs 2.02), reflecting deliberate grouping by domain experts. Open Ontologies covers more of the EPC data schema and applies the BORO 4D pattern more systematically across the domain.

How the hierarchy emerges from building science

The ontology's depth (max 10 levels) is not hand-tuned — it follows the natural classification that building scientists use. The EPC data schema describes heating systems as flat text fields ("Condensing gas boiler with radiators"), but the underlying domain has layered structure:

graph TD
    HS[Heating System] --> CH[Central Heating]
    HS --> NC[Non-Central / Room Heating]

    CH --> WET[Wet Central Heating<br/><i>hydronic distribution</i>]
    CH --> WA[Warm Air Central Heating<br/><i>ducted air</i>]
    CH --> EC[Electric Central Heating<br/><i>storage / underfloor</i>]

    WET --> BB[Boiler-Based]
    WET --> HP[Heat Pump]
    WET --> DH[Community / District]

    BB --> CB[Combustion Boiler]
    BB --> CHP[Micro-CHP]

    CB --> GAS["Gas boiler"]
    CB --> OIL["Oil boiler"]
    CB --> LPG["LPG boiler"]
    CB --> COND["Condensing boiler"]
    CB --> COMBI["Combi boiler"]
    CB --> BACK["Back boiler"]

    HP --> ASHP["Air source"]
    HP --> GSHP["Ground source"]
    HP --> WSHP["Water source"]

    EC --> STOR["Storage heaters"]
    EC --> PNL["Panel heaters"]
    EC --> UF["Underfloor electric"]

    NC --> FIX[Fixed Room Heater]
    NC --> PORT[Portable Heater]

    FIX --> GROOM["Gas room heater"]
    FIX --> EROOM["Electric room heater"]
    FIX --> SFROOM["Solid fuel room heater"]

    style HS fill:#1a1a2e,color:#fff
    style CH fill:#16213e,color:#fff
    style NC fill:#16213e,color:#fff
    style WET fill:#0f3460,color:#fff
    style WA fill:#0f3460,color:#fff
    style EC fill:#0f3460,color:#fff
    style BB fill:#533483,color:#fff
    style HP fill:#533483,color:#fff
    style DH fill:#533483,color:#fff
    style CB fill:#e94560,color:#fff
    style CHP fill:#e94560,color:#fff

The same pattern applies to the building fabric — heat transfer physics dictates the grouping:

graph TD
    TE[Building Thermal Envelope] --> OP[Opaque Elements<br/><i>conduction-dominated</i>]
    TE --> TR[Transparent Elements<br/><i>radiation + conduction</i>]

    OP --> WALL[Walls]
    OP --> ROOF[Roofs]
    OP --> FLOOR[Floors]

    TR --> WIN[Windows]
    TR --> DOOR[Doors]

    WALL --> MAS[Masonry Walls<br/><i>thermal mass</i>]
    WALL --> FRM[Framed Walls<br/><i>stud bridges</i>]

    MAS --> CAV["Cavity wall"]
    MAS --> SOL["Solid brick"]
    MAS --> SND["Sandstone"]
    MAS --> GRN["Granite"]
    MAS --> COB["Cob"]

    FRM --> TF["Timber frame"]
    FRM --> SYS["System-built"]
    FRM --> PH["Park home"]

    ROOF --> PIT[Pitched Roof]
    ROOF --> FLT[Flat Roof]

    PIT --> COLD["Cold roof<br/><i>insulation at ceiling</i>"]
    PIT --> WARM["Warm roof<br/><i>insulation at rafter</i>"]
    PIT --> THATCH["Thatched"]

    WIN --> SGL["Single glazed"]
    WIN --> DBL["Double glazed"]
    WIN --> TPL["Triple glazed"]
    WIN --> SEC["Secondary glazing"]

    style TE fill:#1a1a2e,color:#fff
    style OP fill:#16213e,color:#fff
    style TR fill:#16213e,color:#fff
    style WALL fill:#0f3460,color:#fff
    style ROOF fill:#0f3460,color:#fff
    style FLOOR fill:#0f3460,color:#fff
    style WIN fill:#0f3460,color:#fff
    style DOOR fill:#0f3460,color:#fff
    style MAS fill:#533483,color:#fff
    style FRM fill:#533483,color:#fff
    style PIT fill:#533483,color:#fff
    style FLT fill:#533483,color:#fff

Each level in the tree is a real building science distinction — central vs room heating, hydronic vs warm air, combustion vs electric, masonry vs framed, cavity vs solid. An independent building scientist, given the same EPC data values, produces these same intermediate groupings (verified by clean-room reproduction). RDFS reasoning traverses these chains transitively, which is why a 10-level hierarchy generates 662 inferred triples from 3,229 raw.

EPC Column Coverage Benchmark

Both ontologies tested against 36 key EPC data columns — can each ontology receive and represent the data from that column?

Metric	NDTP/IRIS	Open Ontologies
EPC columns covered	18/36 (50%)	36/36 (100%)
Triples	1,346	3,229

Queries derived from published DESNZ/ONS EPC statistical reports — not from either ontology's class structure. Full benchmark: benchmark/epc/

Use onto_align to map it to other domain ontologies:

onto_load benchmark/generated/ies-building-extension.ttl
onto_align <other-ontology.ttl>

Hierarchy Enforcement — Automated Inference Improvement

The hierarchy enforce pack detects flat spots in any ontology and suggests intermediate grouping classes. This is the same process used to deepen the building extension — now codified as a repeatable tool:

onto_load my-ontology.ttl
onto_enforce --pack hierarchy
# → flags classes with >5 direct children
# → reports max depth, avg depth, hierarchy density

Tested on IES Common (511 classes), the tool found 24 flat spots. A clean-room agent — with no prior context — proposed 38 intermediate grouping classes based solely on the domain meaning of the flagged children:

graph LR
    subgraph Before["IES Common — before"]
        EP1[EventParticipant] --> P1["Prosecutor"]
        EP1 --> P2["Observer"]
        EP1 --> P3["Driver"]
        EP1 --> P4["Supplier"]
        EP1 --> P5["WeaponLocation"]
        EP1 --> P6["...52 direct children"]
    end

    subgraph After["IES Common — after hierarchy enforce"]
        EP2[EventParticipant] --> R[RoleInEvent]
        EP2 --> L[LocationInEvent]
        EP2 --> A[AssetInEvent]
        R --> LR2[LegalRole]
        R --> IR[InvestigativeRole]
        R --> CR[CommercialRole]
        LR2 --> Q1["Prosecutor"]
        LR2 --> Q2["Signatory"]
        IR --> Q3["Observer"]
        IR --> Q4["Investigator"]
        CR --> Q5["Supplier"]
        CR --> Q6["Negotiator"]
        L --> Q7["WeaponLocation"]
        L --> Q8["TargetLocation"]
        A --> Q9["VehicleUsed"]
    end

    style EP1 fill:#e94560,color:#fff
    style EP2 fill:#1a1a2e,color:#fff
    style R fill:#16213e,color:#fff
    style L fill:#16213e,color:#fff
    style A fill:#16213e,color:#fff
    style LR2 fill:#0f3460,color:#fff
    style IR fill:#0f3460,color:#fff
    style CR fill:#0f3460,color:#fff

Metric	Before	After	Change
Classes	511	549	+38
RDFS inferred	3,094	3,422	+328 (+10.6%)

The same tool, applied to any ontology, produces the same kind of improvement. The intermediate classes emerge from domain knowledge — not from reference to any other implementation.

Tools

70+ tools organized by function — available as MCP tools (prefixed onto_) and CLI subcommands:

Category	Tools	Purpose
Core	`validate` `load` `save` `clear` `stats` `query` `diff` `lint` `convert` `status`	RDF/OWL validation, querying, and management
Repository	`repo_list` `repo_load`	Browse and load ontologies from configured `[general] ontology_dirs` directories
Cache	`cache_status` `cache_list` `cache_remove` `unload` `recompile`	On-disk N-Triples compile cache, idle-TTL eviction, per-name management (details)
Marketplace	`marketplace`	Browse and install 32 standard W3C/ISO/industry ontologies
Remote	`pull` `push` `import`	Fetch/push ontologies, resolve owl:imports
Schema	`import-schema` `sql-ingest`	Postgres + DuckDB → OWL + SQL → RDF ingest
Data	`map` `ingest` `shacl` `shacl_check` `vocab_check` `reason` `extend`	Structured data → RDF pipeline; `vocab_check` = closed-world check that generated data uses only ontology-declared terms (catches what open-world SHACL misses)
Versioning	`version` `history` `rollback`	Named snapshots and rollback
Lifecycle	`plan` `apply` `lock` `drift` `enforce` `monitor` `monitor-clear` `lineage`	Terraform-style change management with webhook alerts and OpenCheir governance integration
Alignment	`align` `align_feedback` `align_fuzzy` `align_flora`	Cross-ontology class matching with self-calibrating weights + fuzzy-logic adjudication and end-to-end signal-driven pipeline
HNSW	`hnsw_build`	Persisted HNSW indices (cosine + Poincaré) over class embeddings
Clinical	`crosswalk` `enrich` `validate_clinical`	ICD-10 / SNOMED / MeSH crosswalks (93-row sample ships in `data/crosswalks.parquet`; run `python scripts/build_crosswalks.py` to rebuild or extend)
Feedback	`lint_feedback` `enforce_feedback`	Self-calibrating suppression
Embeddings	`embed` `search` `similarity`	Dual-space semantic search (text + Poincaré structural)
Reasoning	`reason` `dl_explain` `dl_check` `classify_el`	Native OWL2-DL SHOIQ tableaux + OWL-EL classification
Dynamics	`action_register` `action_applicable` `action_apply` `action_list` `action_apply_concurrent` `invariant_register` `invariant_list` `invariant_remove` `invariant_check` `default_register` `default_apply`	Action schemas + concurrent atomic ticks + static causal laws + default values
Causal	`certify_action`	Four-verdict causal certificate (EXECUTE / REJECT / EXPERIMENT / ABSTAIN); optional `causal-pywhy` feature enables backdoor identification
Planner	`plan_compile_pddl` `plan_classical` `plan_validate`	Compile + validate on the server; solver (Fast Downward) is a client-side subprocess
Governance	`policy_register` `policy_list` `policy_check`	Authorisation rules; composes with `certify_action`
RAG	`segment_retrieve` `graph_projection_lossy_check`	TBox-slice retrieval + projection-loss auditor
Extraction	`extract_scaffold` `extract_validate`	Schema-guided structured-extraction prompt + validator
CQs	`cq_run` `verify_cq` `cq_verdicts_list`	Competency-question runner with pitfall hints + judgement loop
Shape induction	`shape_combinatorics` `shape_induce`	Property-combination lattice + data-driven SHACL induction
Borderline loop	`borderline_partition` `borderline_record_verdict`	Generalised two-threshold review pattern for any candidate set
SQL sync	`sql_sync_state` `sql_sync_reset` `sql_sync_states_list`	CDC watermark tracking for incremental SQL ingest
Evaluation	`eval_alignment` `eval_rag` `eval_rag_mmrag`	Alignment P/R/F1 + RAG Hit@k / MRR / faithfulness + dataset adapter

Architecture

Engine

flowchart TD
    subgraph Clients["Clients"]
        Claude["Claude / LLM\nMCP stdio"]
        CLI["CLI\nonto_* subcommands"]
        Studio["Studio\nHTTP REST"]
    end

    subgraph Server["Open Ontologies Server"]
        direction TB

        subgraph Transport["Transport Layer"]
            MCP_HTTP["MCP Streamable HTTP\n/mcp"]
            REST["REST API\n/api/query · /api/update\n/api/save · /api/load · /api/lineage"]
        end

        subgraph ToolGroups["70+ Tools"]
            direction LR
            Core["Core\nvalidate · load · save · clear\nstats · query · diff · lint\nconvert · status"]
            DataPipe["Data Pipeline\nmap · ingest · shacl\nreason · extend · import-schema"]
            Lifecycle["Lifecycle\nplan · apply · lock · drift\nenforce · monitor · lineage"]
            Advanced["Alignment + Clinical\nalign · crosswalk · enrich\nenrich · embed · search · similarity\ndl_explain · dl_check"]
            Version["Versioning\nversion · history · rollback"]
        end

        subgraph Core2["Core Engine"]
            GraphStore["Oxigraph Triple Store\nRDF/OWL in-memory\nSPARQL 1.1"]
            SQLite["SQLite\nlineage events\nversion snapshots\nlint/enforce feedback\nembedding vectors"]
            Reasoner["OWL2-DL Reasoner\nSHOIQ tableaux\nRDFS · OWL-RL"]
            Embedder["Embedding Engine\ntract-onnx (O

Topic	Link
IES Ecosystem Demo	docs/ies-ecosystem.md
SPARQL Examples	docs/ies-examples.md
Building Alignment	docs/ies-alignment.md

Open Ontologies

README

What's New (three-layer architecture + 13 new primitives)

Three-layer architecture

13 new primitives

Validating end-to-end

Quick Start (MCP / CLI)

Install

Connect to your MCP client

Build your first ontology

Studio (Desktop App)

Why virtualized tree (not 3D graph)

How it works

Install and run

Features

Keyboard shortcuts

Benchmarks

OntoAxiom — LLM Axiom Identification

Pizza Ontology — Manchester Tutorial

`/sketch` vs `/build` — Two Build Modes

Mushroom Classification — OWL Reasoning vs Expert Labels

Ontology Marketplace — 32 Standard Ontologies

Reasoning Performance — vs HermiT

OAEI Ontology Alignment — Anatomy Track

IES Support

The IES Layers

Benchmark

Example Data

SHACL Validation

Data Mapping: EPC → IES

IES Building Extension — Comparison with NDTP/IRIS

How the hierarchy emerges from building science

EPC Column Coverage Benchmark

Hierarchy Enforcement — Automated Inference Improvement

Further Reading

Tools

Architecture

Engine

Comments (0)

Open Ontologies

README

What's New (three-layer architecture + 13 new primitives)

Three-layer architecture

13 new primitives

Validating end-to-end

Quick Start (MCP / CLI)

Install

Connect to your MCP client

Build your first ontology

Studio (Desktop App)

Why virtualized tree (not 3D graph)

How it works

Install and run

Features

Keyboard shortcuts

Benchmarks

OntoAxiom — LLM Axiom Identification

Pizza Ontology — Manchester Tutorial

/sketch vs /build — Two Build Modes

Mushroom Classification — OWL Reasoning vs Expert Labels

Ontology Marketplace — 32 Standard Ontologies

Reasoning Performance — vs HermiT

OAEI Ontology Alignment — Anatomy Track

IES Support

The IES Layers

Benchmark

Example Data

SHACL Validation

Data Mapping: EPC → IES

IES Building Extension — Comparison with NDTP/IRIS

How the hierarchy emerges from building science

EPC Column Coverage Benchmark

Hierarchy Enforcement — Automated Inference Improvement

Further Reading

Tools

Architecture

Engine

Comments (0)

`/sketch` vs `/build` — Two Build Modes