heshamfs/materials-simulation-skills/ontology-mapper

Ontology Mapper

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 3.10+
• 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

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"

Ontology scope — crystal/sample annotation is CMSO-only. crystal_mapper.py

and sample_annotator.py emit crystal-structure vocabulary (Crystalline Material,

Crystal Structure, Unit Cell, Space Group, lattice properties). This vocabulary is

defined by CMSO. **ASMO is a simulation-methods ontology and does not define any

crystal/sample classes** — so for ASMO use the concept-mapping path

(concept_mapper.py, which resolves terms like DFT, NPT, timestep, PBE to real ASMO

classes) only. If sample_annotator.py/crystal_mapper.py is run with an ontology

whose summary lacks the required classes (e.g. --ontology asmo), each unresolvable

term is flagged in results.validation_warnings and given confidence: 0.0 rather

than silently emitting an invalid term.

Script Outputs (JSON Fields)

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, create a <name>_mappings.json in references/:

{
  "ontology": "myonto",
  "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": "myonto_mappings.json" to the ontology's entry in ontology_registry.json. No code changes needed.

Only include the `sample_schema`, `crystal_output`, `material_type_rules` and `annotation_routing` blocks if every class/property they name actually exists in that ontology's summary. sample_annotator.py validates emitted terms against the loaded summary and flags any that are undefined (results.validation_warnings, confidence: 0.0). For example, asmo_mappings.json deliberately ships only synonyms and property_synonyms because ASMO is a simulation-methods ontology with no crystal/sample vocabulary — its concept terms (DFT, NPT, timestep, PBE) all resolve, but a crystal/sample config would emit unresolvable terms.

Error Handling

Interpretation Guidance

• Confidence scores: 1.0 = exact label match, 0.9 = synonym-table match, 0.7 = substring match, 0.5 = description match. Note: the per-ontology synonym table is consulted before exact-label matching, so a term that is both a synonym key and a class label (e.g. space group, unit cell, atom) is reported as a 0.9 synonym match even though it coincides exactly with a class label — the matched class and IRI are still correct.
• `sample_annotator.py` validation warnings: every emitted class/property is checked against the loaded ontology summary. Terms not defined in that ontology are flagged in results.validation_warnings (and the corresponding annotation gets a validation_warning field with confidence: 0.0). This is how the annotator signals that a crystal/sample term cannot resolve to an IRI in the chosen ontology (e.g. running --ontology asmo on a crystalline sample — see below).
• 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.

Verification checklist

• [ ] Confirmed results.validation_warnings is empty (or every entry is explained) — a non-empty list means an emitted class/property is not defined in the chosen ontology and was given confidence: 0.0; do not report such terms as valid annotations.
• [ ] Recorded the match_type and confidence for each concept match and confirmed the chosen term is acceptable for its tier (1.0 exact, 0.9 synonym, 0.7 substring, 0.5 description); for any substring_* or description_class match, verified the matched class is actually the intended concept and not an incidental string hit.
• [ ] For crystal mappings, recorded results.effective_system and results.bravais_lattice (the resolved Pearson symbol, e.g. cF/cI), and confirmed the input Bravais/space-group/system are mutually consistent (no "space group N implies X but Y was specified" warning in validation_warnings).
• [ ] Checked lattice-parameter constraints against effective_system — confirmed no warnings such as "Cubic requires a=b" / angle-90 violations, or explicitly justified each one (warnings are advisory, the mapping still proceeds).
• [ ] Listed results.unmatched (concept) and results.unmapped_fields (sample) and confirmed nothing materially important was silently dropped; ran the emitted class_browser.py suggestion for any unmatched term that should have resolved.
• [ ] Reviewed results.suggested_properties and recorded which missing fields (elements, space_group, lattice_a, ...) are intentionally omitted vs. should be added before the annotation is considered complete.

Common pitfalls & rationalizations