7cf10084b4
- Added `fix_dual_class_link.py` to remove dual class link references from specified YAML files. - Created `fix_specific_ghosts.py` to apply specific replacements in YAML files based on defined mappings. - Introduced `migrate_staff_count.py` to migrate staff count references to a new structure in specified YAML files. - Developed `migrate_type_slots.py` to replace type-related slots with new identifiers across YAML files. - Implemented `scan_ghost_references.py` to identify and report ghost references to archived slots and classes in YAML files. - Added `verify_ontology_terms.py` to verify the presence of ontology terms in specified ontology files against schema definitions.
189 lines
6.4 KiB
YAML
189 lines
6.4 KiB
YAML
# ConversionRate - Structured conversion rate measurement
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# Created per slot_fixes.yaml migration for: visitor_conversion_rate
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# Creation date: 2026-01-14
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#
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# REVISION FROM slot_fixes.yaml (lines 1646-1669):
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# - label: has_or_had_conversion_rate (slot)
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# - label: ConversionRate (class) ← THIS FILE
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# - link_branch 1: has_or_had_type → ConversionRateType → ConversionRateTypes
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# - link_branch 2: temporal_extent → TimeSpan
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id: https://nde.nl/ontology/hc/classes/ConversionRate
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name: ConversionRate
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title: ConversionRate
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prefixes:
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linkml: https://w3id.org/linkml/
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hc: https://nde.nl/ontology/hc/
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schema: http://schema.org/
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xsd: http://www.w3.org/2001/XMLSchema#
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imports:
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- linkml:types
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- ./ConversionRateType
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- ./ConversionRateTypes
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- ./TimeSpan
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- ../slots/has_or_had_type
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- ../slots/temporal_extent
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default_range: string
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classes:
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ConversionRate:
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description: >-
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Structured representation of a conversion rate metric for heritage institutions.
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**WHY A DEDICATED CLASS?**
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Conversion rates are more than simple floats - they need:
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- **Type**: What kind of conversion (visitor-to-purchase, visitor-to-member)
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- **Value**: The actual percentage (0.35 = 35%)
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- **Time Period**: When this rate was measured
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- **Context**: Comparison benchmarks, notes
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**SLOT_FIXES.YAML REVISION** (lines 1646-1669):
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- Branch 1: has_or_had_type → ConversionRateType/ConversionRateTypes
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- Branch 2: temporal_extent → TimeSpan (measurement period)
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**USE CASES**:
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- Gift shop performance: 35% visitor-to-purchase conversion
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- Membership drives: 5% visitor-to-member conversion
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- Digital engagement: 3% website-to-visit conversion
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**ONTOLOGY MAPPING**:
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- class_uri: schema:QuantitativeValue (numeric measurement)
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- Uses schema:interactionStatistic for behavioral metrics
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class_uri: schema:QuantitativeValue
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exact_mappings:
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- schema:QuantitativeValue
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close_mappings:
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- schema:interactionStatistic
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slots:
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- has_or_had_type
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- temporal_extent
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attributes:
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rate_value:
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range: float
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required: true
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description: >-
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The conversion rate as a decimal (0.35 = 35%).
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Always expressed as proportion between 0.0 and 1.0.
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minimum_value: 0.0
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maximum_value: 1.0
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rate_percentage:
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range: float
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description: >-
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The conversion rate as a percentage (35 = 35%).
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Calculated field: rate_value × 100.
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minimum_value: 0.0
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maximum_value: 100.0
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sample_size:
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range: integer
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description: >-
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The number of observations in the denominator (total visitors, emails sent, etc.).
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Required for statistical significance assessment.
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converted_count:
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range: integer
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description: >-
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The number of successful conversions (numerator).
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Required for statistical significance assessment.
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measurement_period_description:
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range: string
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description: >-
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Human-readable description of the measurement period.
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Examples: "Q1 2025", "Calendar Year 2024", "Exhibition period"
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benchmark_comparison:
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range: string
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description: >-
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How this rate compares to industry benchmarks.
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Examples: "Above average", "Top quartile", "Below benchmark"
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notes:
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range: string
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description: >-
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Additional context about this conversion rate measurement.
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Examples: Special circumstances, data collection methodology.
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slot_usage:
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has_or_had_type:
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range: ConversionRateType
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inlined: true
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description: >-
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The type of conversion being measured.
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Use concrete types from ConversionRateTypes (VisitorToPurchaseConversion, etc.)
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examples:
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- value: |
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has_or_had_type:
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has_or_had_label: "Visitor to Purchase"
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source_population: "museum visitors"
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target_action: "made gift shop purchase"
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description: Visitor to purchase conversion type
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temporal_extent:
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range: TimeSpan
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inlined: true
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description: >-
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The time period during which this conversion rate was measured.
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Use begin/end dates to specify the measurement window.
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examples:
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- value: |
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temporal_extent:
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begin_of_the_begin: "2024-01-01"
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end_of_the_end: "2024-12-31"
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description: Calendar year 2024 measurement period
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annotations:
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custodian_types: '["*"]'
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custodian_types_rationale: >-
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Conversion rates applicable to all custodian types with retail or engagement.
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custodian_types_primary: "*"
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specificity_score: 0.45
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specificity_rationale: >-
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Moderate specificity - relevant to custodians tracking performance metrics.
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examples:
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- value: |
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ConversionRate:
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rate_value: 0.35
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rate_percentage: 35.0
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sample_size: 500000
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converted_count: 175000
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has_or_had_type:
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has_or_had_label: "Visitor to Purchase"
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temporal_extent:
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begin_of_the_begin: "2024-01-01"
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end_of_the_end: "2024-12-31"
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measurement_period_description: "Calendar Year 2024"
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benchmark_comparison: "Above average (industry avg 15-25%)"
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description: Gift shop 35% visitor-to-purchase conversion rate.
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- value: |
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ConversionRate:
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rate_value: 0.05
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rate_percentage: 5.0
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sample_size: 100000
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converted_count: 5000
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has_or_had_type:
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has_or_had_label: "Visitor to Member"
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temporal_extent:
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begin_of_the_begin: "2024-06-01"
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end_of_the_end: "2024-08-31"
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measurement_period_description: "Summer membership drive"
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benchmark_comparison: "Strong (industry avg 1-3%)"
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description: Summer membership campaign with 5% conversion.
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comments:
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- Created from slot_fixes.yaml migration (2026-01-14)
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- Replaces simple float visitor_conversion_rate slot
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- Supports multiple conversion types with industry benchmarks
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- Temporal extent enables trend analysis over time
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