Ontology Mapper

SkillDev tools

Map materials science terms, crystal structures, and sample descriptions to ontology classes and properties. Supports any ontology registered in ontology_registry.json. Use when translating natural-language material descriptions to ontology terms, annotating simulation inputs with ontology metadata, or mapping crystal parameters (space group, Bravais lattice, lattice constants) to standardized ontology representations.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Ontology Mapper skill

What this skill tells your AI

The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/ontology-mapper/SKILL.md and read by ahel’s review.

Goal

Translate real-world materials science descriptions into standardized ontology annotations. Given terms like "FCC copper" or structured data like {"material": "iron", "structure": "BCC", "lattice_a": 2.87}, produce the corresponding ontology classes and properties for any registered ontology.

Requirements

  • Python 3.8+
  • No external dependencies (Python standard library only)
  • Requires ontology-explorer's summary JSON and ontology_registry.json
  • Per-ontology mapping config (<name>_mappings.json) for ontology-specific synonyms and labels

Inputs to Gather

InputDescriptionExample
OntologyOntology name from registrycmso, asmo
Term(s)Natural-language materials concept(s)"unit cell", "FCC,copper,lattice"
Crystal systemOne of the 7 crystal systemscubic, hexagonal
Bravais latticeLattice type (symbol or common name)FCC, cF, BCC
Space groupSpace group number (1-230)225
Lattice parametersa, b, c in angstroms; alpha, beta, gamma in degreesa=3.615
Sample descriptionJSON dict with material properties{"material":"copper","structure":"FCC"}

Decision Guidance

What do you need to map?
├── A concept or term to find its ontology class
│   └── concept_mapper.py --ontology <name> --term "<term>"
├── Crystal structure parameters to ontology terms
│   └── crystal_mapper.py --ontology <name> --bravais <type> --space-group <N> --a <val>
├── A full sample description to ontology annotations
│   └── sample_annotator.py --ontology <name> --sample '<json>'
└── Multiple terms at once
    └── concept_mapper.py --ontology <name> --terms "term1,term2,term3"

Script Outputs (JSON Fields)

ScriptKey Outputs
scripts/concept_mapper.pyresults.matches, results.unmatched, results.suggestions
scripts/crystal_mapper.pyresults.ontology_classes, results.ontology_properties, results.effective_system, results.bravais_lattice, results.validation_warnings
scripts/sample_annotator.pyresults.annotations, results.sample_type, results.material_type, results.unmapped_fields, results.suggested_properties

Workflow

  1. If the user provides natural-language terms, use concept_mapper.py to find matching ontology classes.
  2. If the user describes crystal structure parameters, use crystal_mapper.py to map them and validate constraints.
  3. For a complete sample description, use sample_annotator.py to produce full ontology annotations.
  4. Review any validation warnings (e.g., lattice parameter mismatches for the crystal system).
  5. Check unmapped_fields and suggested_properties for completeness.

Conversational Workflow Example

User: I'm setting up an MD simulation of BCC iron with lattice parameter 2.87 angstroms.
      What CMSO terms should I use?

Agent: Let me map your iron sample to CMSO ontology terms.

[Runs: sample_annotator.py --ontology cmso --sample '{"material":"iron","structure":"BCC","lattice_a":2.87,"space_group":229}' --json]

Your BCC iron simulation maps to these CMSO annotations:

- **Sample**: Atomic Scale Sample (subclass of Computational Sample)
- **Material**: Crystalline Material
- **Unit Cell**: Bravais lattice = "cI" (body-centered cubic)
- **Space Group**: number = 229 (Im-3m)
- **Lattice**: a = 2.87 Å
- **Element**: Fe

Suggested additions:
- Number of atoms in the simulation cell
- Simulation cell vectors and angles

CLI Examples

# Map a single concept
python3 skills/ontology/ontology-mapper/scripts/concept_mapper.py \
  --ontology cmso --term "space group" --json

# Map multiple terms
python3 skills/ontology/ontology-mapper/scripts/concept_mapper.py \
  --ontology cmso --terms "FCC,copper,lattice constant" --json

# Map crystal parameters (with ontology-specific labels)
python3 skills/ontology/ontology-mapper/scripts/crystal_mapper.py \
  --ontology cmso --bravais FCC --space-group 225 --a 3.615 --json

# Map crystal parameters (generic labels, no ontology specified)
python3 skills/ontology/ontology-mapper/scripts/crystal_mapper.py \
  --bravais FCC --space-group 225 --a 3.615 --json

# Annotate a full sample
python3 skills/ontology/ontology-mapper/scripts/sample_annotator.py \
  --ontology cmso \
  --sample '{"material":"copper","structure":"FCC","space_group":225,"lattice_a":3.615}' \
  --json

Adding a New Ontology

To support a new ontology (e.g., ASMO), create a <name>_mappings.json in references/:

{
  "ontology": "asmo",
  "synonyms": { "simulation method": "Simulation Method", ... },
  "property_synonyms": { "timestep": "has timestep", ... },
  "material_type_rules": { "keyword_rules": [...], "default": "Material" },
  "sample_schema": { "sample_class": "Simulation", ... },
  "crystal_output": { "base_classes": [...], "property_map": {...} },
  "annotation_routing": { "unit_cell_indicators": [...], ... }
}

Then add "mappings_file": "asmo_mappings.json" to the ontology's entry in ontology_registry.json. No code changes needed.

Error Handling

ErrorCauseResolution
space_group must be between 1 and 230Invalid space group numberUse a valid space group number
a must be positiveNon-positive lattice parameterProvide positive values in angstroms
Sample must be a non-empty dictEmpty or missing sample dataProvide a valid JSON sample dict
Validation warningsLattice parameters inconsistent with crystal systemCheck that a=b=c for cubic, etc.

Interpretation Guidance

  • Confidence scores: 1.0 = exact match, 0.9 = synonym match, 0.7 = substring match, 0.5 = description match
  • Validation warnings: indicate potential mistakes (e.g., specifying a!=b for cubic). These are warnings, not errors — the mapping still proceeds.
  • Unmapped fields: input keys that the annotator doesn't recognize. These may need manual mapping.
  • Suggested properties: additional ontology properties that would make the annotation more complete.

Limitations

  • Concept mapping uses string matching and a per-ontology synonym table; it does not understand arbitrary natural language
  • Crystal system validation checks basic constraints only (not all crystallographic rules)
  • The element resolver recognizes common element names and symbols but may miss unusual spellings
  • Bravais lattice aliases cover common usage (FCC, BCC, HCP) but not all crystallographic notation variants

References

Version History

DateVersionChanges
2026-02-251.1Refactored for multi-ontology support: externalized CMSO-specific knowledge to config
2026-02-251.0Initial release with CMSO mapping support
Advanced
Item type
skill
Key
ontology-mapper-freedomintelligence
Source
github.com/freedomintelligence/openclaw-medical-skills