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Anti-Hallucination Guardrails

A key use case for linkml-term-validator is preventing AI systems from hallucinating ontology identifiers.

The Problem

Language models frequently hallucinate identifiers like gene IDs, ontology terms, and other structured references. These fake identifiers often appear structurally correct (e.g., GO:9999999, CHEBI:88888) but don't actually exist in the source ontologies.

This creates serious data quality issues: - Invalid references that break data integration - Nonsense annotations that corrupt curated datasets - False confidence in AI-generated content

The Solution: Dual Validation

A robust guardrail requires dual validation—forcing the AI to provide both the identifier AND its canonical label, then validating that they match:

Instead of accepting:

term: GO:0005515  # Single piece of information - easy to hallucinate

Require and validate:

term:
  id: GO:0005515
  label: protein binding  # Must match canonical label in ontology

This dramatically reduces hallucinations because the AI must get two interdependent facts correct simultaneously, which is significantly harder to fake convincingly than inventing a single plausible-looking identifier.

Implementation Pattern

1. Define Schemas with Binding Constraints

classes:
  GeneAnnotation:
    slots:
      - gene
      - go_term
    slot_usage:
      go_term:
        range: GOTerm
        bindings:
          - binds_value_of: id
            range: BiologicalProcessEnum

  GOTerm:
    slots:
      - id        # AI must provide both
      - label     # fields correctly

enums:
  BiologicalProcessEnum:
    reachable_from:
      source_ontology: sqlite:obo:go
      source_nodes:
        - GO:0008150  # biological_process
      relationship_types:
        - rdfs:subClassOf

2. Validate AI-Generated Outputs Before Committing

from linkml.validator import Validator
from linkml_term_validator.plugins import BindingValidationPlugin

# Create validator with label checking enabled
plugin = BindingValidationPlugin(
    oak_adapter_string="sqlite:obo:",
    validate_labels=True
)
validator = Validator(schema="schema.yaml", validation_plugins=[plugin])

# Validate AI-generated data
report = validator.validate_source(loader, target_class="GeneAnnotation")

if len(report.results) > 0:
    # Reject hallucinated terms, prompt AI to regenerate
    for result in report.results:
        print(f"ERROR: {result.message}")
    raise ValueError("Invalid ontology terms detected")

3. Use Validation During Generation

The most effective approach embeds validation during AI generation rather than treating it as a filtering step afterward. This transforms hallucination resistance from a detection problem into a generation constraint.

Pattern: 1. AI generates structured output with id+label pairs 2. Validate immediately with BindingValidationPlugin(validate_labels=True) 3. If validation fails, provide error messages back to AI with retry opportunity 4. Only accept outputs that pass validation

What Gets Validated

The BindingValidationPlugin with validate_labels=True checks:

  1. ID exists in ontology: The identifier (e.g., GO:0005515) is a real term
  2. ID matches constraint: The term satisfies the binding's range constraint (e.g., is a biological process)
  3. Label matches ontology: The provided label matches the canonical label from the ontology

All three checks must pass.

Real-World Benefits

  • Prevents fake identifiers from entering curated datasets
  • Catches label mismatches where AI uses real IDs but wrong labels
  • Validates dynamic constraints (e.g., only disease terms, only neuron types)
  • Enables reliable automation of curation tasks traditionally requiring human experts

Example: Invalid AI Output

annotations:
  - gene: BRCA1
    go_term:
      id: GO:0005515
      label: DNA binding  # ❌ WRONG - actual label is "protein binding"

Validation result:

ERROR: Label mismatch for GO:0005515
  Expected: protein binding
  Found: DNA binding

Example: Hallucinated ID

annotations:
  - gene: BRCA1
    go_term:
      id: GO:9999999  # ❌ Doesn't exist
      label: cell stuff

Validation result:

ERROR: GO:9999999 not found in Gene Ontology

CLI Usage

# Validate with label checking enabled
linkml-term-validator validate-data data.yaml \
  --schema schema.yaml \
  --labels  # Enable label validation

Best Practices

  1. Always use dual validation (id + label) for AI-generated ontology references
  2. Provide clear error messages back to the AI when validation fails
  3. Use dynamic enums to constrain valid terms (e.g., only disease terms)
  4. Cache ontology labels for fast validation during generation
  5. Validate early and often - don't wait until after bulk generation

Learn More

For detailed patterns and best practices on making ontology IDs hallucination-resistant in AI workflows, see:

See Also