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IES Ecosystem Demo

The Information Exchange Standard (IES) is a layered ontology framework used by the UK National Digital Twin Programme (NDTP). Open Ontologies supports the full IES stack through its marketplace.

The IES Layers

IES is split into three tiers, each building on the last:

ies-top     (ToLO)    ~22 classes    BORO foundations, 4D extensionalism
    |
ies-core              ~131 classes   Persons, states, events, identifiers
    |
ies-common            ~513 classes   Full ontology — everything above + domain patterns

Quick Start: Load the Full Stack

# Load all three layers individually
onto_marketplace install ies-top
onto_stats
onto_clear

onto_marketplace install ies-core
onto_stats
onto_clear

# Or load the full IES Common (includes everything)
onto_marketplace install ies
onto_stats

Full Pipeline Demo

Step 1: Load and Validate

onto_marketplace install ies
onto_stats
onto_lint

Expected output:

  • 513 classes counted structurally (511 declared with rdfs:Class), 162 object properties and 44 datatype properties for 206 in total, 4,039 triples
  • 19 lint issues, all info-level missing_domain notices, and no warnings or errors

Step 2: Reason

onto_reason --profile rdfs
onto_stats

RDFS materialises +3,094 inferred triples (77% growth) — transitive subclass chains across 241 State subclasses, 117 ClassOfEntity subclasses, and 102 Event subclasses.

Step 3: Ingest Real EPC Data

The repo includes a sample of 10 real UK Energy Performance Certificates from the Open Data Communities register (benchmark/epc/epc-sample.csv) with a pre-built mapping config (benchmark/epc/epc-ies-mapping.json).

# Load IES Building Extension as the target ontology
onto_validate benchmark/generated/ies-building-extension.ttl
onto_load benchmark/generated/ies-building-extension.ttl

# Generate or review the mapping (CSV columns → IES predicates)
onto_map benchmark/epc/epc-sample.csv

# Ingest the CSV data using the mapping
onto_ingest benchmark/epc/epc-sample.csv --mapping benchmark/epc/epc-ies-mapping.json

# Check what was created
onto_stats

# Reason to materialise inferred triples
onto_reason --profile rdfs

# Query: which dwellings have poor energy ratings?
onto_query "SELECT ?dwelling ?rating WHERE {
  ?dwelling a <http://example.org/ontology/ies-building#Dwelling> .
  ?dwelling <http://example.org/ontology/ies-building#energyRatingBand> ?rating .
  FILTER(?rating IN (
    <http://example.org/ontology/ies-building#BandE>,
    <http://example.org/ontology/ies-building#BandF>,
    <http://example.org/ontology/ies-building#BandG>
  ))
}"

The mapping config maps CSV columns to IES Building predicates:

CSV Column IES Predicate Type
postcode bldg:hasPostalCode string
propertytype ies:similarEntity → ClassOfBuilding lookup (D→Detached, S→Semi, T→Terraced, F→Flat)
CURRENT_ENERGY_RATING bldg:energyRatingBand lookup (A-G → BandA-BandG)
CURRENT_ENERGY_EFFICIENCY bldg:energyScore integer
MAIN_FUEL bldg:usesFuel → FuelType lookup (mains gas→MainsGas, etc.)
MAINHEAT_DESCRIPTION bldg:heatingDescription string
WALLS_DESCRIPTION bldg:wallsDescription string
inspectiondate bldg:inspectionDate date
CO2_EMISSIONS_CURRENT bldg:co2Emissions decimal

This mirrors NDTP's actual pipeline: take tabular EPC data, map it to IES-shaped RDF, validate, reason, and query.

Step 4: Explore with SPARQL

See ies-examples.md for 5 ready-to-use queries. Quick taste:

# Count State subclasses (the 4D temporal pattern)
onto_query "SELECT (COUNT(DISTINCT ?c) AS ?count) WHERE {
  ?c rdfs:subClassOf* <http://informationexchangestandard.org/ont/ies/common/State> .
  FILTER(?c != <http://informationexchangestandard.org/ont/ies/common/State>)
}"
# Result: 241

Step 5: Load Example Data

IES has ~42 example Turtle files across its repos. Pull them directly:

# Event participation patterns
onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/examples/sample-data/event-participation.ttl

# Hospital scenario
onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/examples/sample-data/hospital.ttl

# Movement tracking
onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/examples/sample-data/movement.ttl

# GeoSPARQL integration
onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/examples/sample-data/geosparql.ttl

Additional examples from telicent-oss:

# Ship movement (4D spatiotemporal)
onto_pull https://raw.githubusercontent.com/telicent-oss/ies-examples/main/additional_examples/ship_movement.ttl

# Aircraft observation
onto_pull https://raw.githubusercontent.com/telicent-oss/ies-examples/main/additional_examples/observing_moving_aircraft.ttl

# Countries reference data
onto_pull https://raw.githubusercontent.com/telicent-oss/ies-examples/main/regions_of_the_world/countries.ttl

Step 6: SHACL Validation

IES provides SHACL shapes for data validation:

# Validate against IES Common shapes
onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/specification/ies-common.shacl
onto_shacl

Core-level shapes for specific patterns:

# Person state validation
onto_pull https://raw.githubusercontent.com/IES-Org/ies-core/main/spec/validation_artefacts/PersonState.shacl.ttl

# Birth/death state validation
onto_pull https://raw.githubusercontent.com/IES-Org/ies-core/main/spec/validation_artefacts/BirthStateShape.shacl.ttl
onto_pull https://raw.githubusercontent.com/IES-Org/ies-core/main/spec/validation_artefacts/DeathStateShape.shacl.ttl

Step 7: Alignment

Map IES to other domain ontologies:

# Save IES, load target, align
onto_save /tmp/ies.ttl
onto_clear
onto_marketplace install schema-org
onto_save /tmp/schema-org.ttl
onto_clear
onto_align --source /tmp/ies.ttl --target /tmp/schema-org.ttl

See ies-alignment.md for the full IES:Building alignment walkthrough.

IES Ecosystem Map

Resource Repo What's There
IES Top (ToLO) IES-Org/ies-top Foundational ontology + 2 SHACL shapes
IES Core IES-Org/ies-core Core patterns + 6 SHACL shapes + 7 test data files
IES Common IES-Org/ont-ies Full ontology (4 formats) + SHACL shapes + 15 examples + user guides + 70 diagrams
IES Examples telicent-oss/ies-examples 28 Turtle example files + regions reference data
IES Tool telicent-oss/ies-tool Python library + bundled v4.3 ontology + SHACL shapes
RDF Transform telicent-oss/telicent-rdf-transform IES4 to IES-Next migration mappings
NDTP AI Extension National-Digital-Twin/ndtp-ai-ontology-extension LLM-driven ontology extension tooling
IES4 (archived, last MIT release v4.3.1) dstl/IES4 Legacy mirror; archived 4 Mar 2025 when custodianship moved from Dstl to DBT. Last public release 4.3.1 (3 Mar 2025) under MIT — pinnable as a reproducible compliance baseline. 4.3.2+ work continues in a private working group. Canonical successor is IES-Org/ont-ies (above), portal at informationexchangestandard.org.

Benchmark Results

Layer Classes Properties Triples + RDFS Fetch RDFS
IES Top (ToLO) ~22 TBD TBD TBD TBD TBD
IES Core ~131 TBD TBD TBD TBD TBD
IES Common 513 206 4,039 +3,094 not measured 63ms

IES Common is the second-largest ontology in the marketplace by class count, after Schema.org, which the marketplace benchmark records at 1,032 classes. RDFS reasoning adds 77% more triples — the richest inference gain of any non-general ontology, driven by the deep 4D State/Event/ClassOfEntity hierarchies.