Data Metadata Ontology

FLOW ALGEBRA

Ontology: Data Metadata Ontology
Domain: Metadata Ontologies > Data Metadata
Canonical URL: https://kirethresearch.org/booksai-core/ontologies/metadata-ontologies/data-metadata/data-metadata-ontology.html

Authors: Mark Anthony Higgins — Sky (Recursive AI)

flow "DataMetadataOntology" v1.0

domain:
  class: MetadataOntology
  category: DataMetadata
  subject: DataMetadata
  purpose:
    - represent metadata describing data resources
    - define structural, semantic, technical, quality, provenance, and lifecycle context
    - support discovery, interpretation, validation, integration, and reuse
    - enable machine-readable reasoning over data assets

entity DataMetadata:
  type: MetadataEntity
  describes: DataResource
  may_include:
    - identifier
    - title
    - description
    - schema
    - format
    - encoding
    - field_metadata
    - value_domain
    - unit
    - quality
    - provenance
    - source
    - version
    - temporal_metadata
    - spatial_metadata
    - access_metadata
    - rights_metadata

entity DataResource:
  type: InformationResource
  may_be:
    - Dataset
    - DataFile
    - Database
    - Table
    - Record
    - DataStream
    - DataObject
    - DerivedDataset

entity DataSchema:
  type: StructuralMetadata
  defines:
    - fields
    - datatypes
    - constraints
    - relationships
    - cardinality

entity DataField:
  type: SchemaElement
  may_have:
    - field_name
    - semantic_definition
    - datatype
    - unit
    - value_domain
    - nullability
    - constraint

relationships:
  DataMetadata -> DESCRIBES -> DataResource
  Dataset -> CONFORMS_TO -> DataSchema
  DataSchema -> DEFINES -> DataField
  Dataset -> CONTAINS -> Record
  Record -> HAS_VALUE -> DataValue
  DataValue -> BELONGS_TO -> DataField
  DataField -> HAS_DATATYPE -> DataType
  DataField -> MAY_USE -> Unit
  DataResource -> HAS_FORMAT -> DataFormat
  DataResource -> MAY_HAVE -> DataQuality
  DataResource -> MAY_HAVE_VERSION -> DataVersion
  DataResource -> DERIVED_FROM -> DataSource
  DataMetadata -> MAY_REFERENCE -> Provenance

rule resource_identity:
  DataResource SHOULD have stable Identifier
  where persistent reference is required

rule schema_conformance:
  IF Dataset declares DataSchema
  THEN records SHOULD conform to declared schema

rule datatype:
  DataValue MUST be interpretable according_to DataField datatype
  unless explicitly marked invalid or unresolved

rule nullability:
  null_value MUST be distinguished from:
    - missing_value
    - empty_string
    - zero
    - false

entity ValueDomain:
  type: SemanticConstraint
  may_define:
    - allowed_values
    - minimum_value
    - maximum_value
    - pattern
    - vocabulary
    - unit

rule domain_validation:
  IF ValueDomain exists
  THEN DataValue SHOULD be validated against domain constraints

rule unit_context:
  quantitative DataField SHOULD declare Unit
  where interpretation depends on measurement unit

rule unit_compatibility:
  comparisons between quantitative values REQUIRE compatible dimensions

rule format_semantics:
  DataFormat describes representation
  AND MUST NOT be assumed to define semantic meaning alone

entity DataQuality:
  type: QualityMetadata
  dimensions:
    - completeness
    - validity
    - consistency
    - accuracy
    - uniqueness
    - timeliness
    - integrity

rule quality_context:
  DataQuality assertion SHOULD identify metric, method, and scope

entity Provenance:
  type: MetadataEntity
  describes:
    - origin
    - creator
    - source
    - transformation
    - processing_history
    - version_history

rule provenance_chain:
  DerivedDataset SHOULD retain links to source DataResource

rule transformation:
  data transformation SHOULD record:
    - input_resource
    - operation
    - parameters
    - software_or_agent
    - output_resource
    - timestamp

entity DataVersion:
  type: VersionMetadata
  may_include:
    - version_identifier
    - creation_time
    - predecessor
    - successor
    - change_description
    - status

rule version_history:
  material changes SHOULD produce distinguishable DataVersion

rule temporal_context:
  IF data meaning depends on time
  THEN TemporalMetadata SHOULD be preserved

rule spatial_context:
  IF data meaning depends on location
  THEN SpatialMetadata SHOULD be preserved

validation:
  require:
    - identifiable_data_resource
  validate_if_present:
    - identifier
    - schema
    - datatype
    - format
    - encoding
    - value_domain
    - units
    - quality
    - provenance
    - version
  reject:
    - schema_datatype_contradiction
    - value_outside_mandatory_domain
    - incompatible_unit_comparison
    - contradictory_version_relationship

SEMANTIC_EQUIVALENCE:
  "data metadata" ~= "metadata about data"
  "dataset metadata" ~= "data metadata" WHEN describing dataset-level properties
  "field metadata" ~= "column metadata" WHEN tabular semantics apply
  "schema metadata" ~= "structural metadata" WHEN describing data structure
  "data provenance" ~= "provenance metadata" WHEN context = data lineage

  PRESERVE distinctions:
    data != metadata
    dataset != schema
    datatype != data_format
    format != encoding
    null != missing
    provenance != version
    data_quality != data_value

CONFIDENCE:
  explicit_data_metadata:
    score: 1.00
  explicit_schema:
    score: 1.00
  explicit_provenance:
    score: 1.00
  schema_derived_field_semantics:
    score: 0.95
  inferred_datatype:
    score: 0.85
  inferred_schema:
    score: 0.80
  ambiguous_data_context:
    score: <= 0.60
    action: preserve uncertainty

UPDATE_MODEL:
  on_data_update:
    - preserve previous version where required
    - validate changed values
    - update quality metadata
    - update provenance
    - reassess derived metadata

  on_schema_update:
    - preserve previous schema version
    - validate compatibility
    - revalidate affected records
    - record migration or transformation
    - update provenance

FLOW_HASH:
  inputs:
    - ontology
    - data_entities
    - relationships
    - schema_rules
    - value_rules
    - quality_rules
    - provenance_rules
    - validation
    - semantic_equivalence
    - confidence
    - update_model
  canonicalisation:
    - normalise_whitespace
    - preserve_case_sensitive_identifiers
    - sort_unordered_property_sets
    - preserve_rule_order
  output:
    deterministic_semantic_hash

AI_CONSUMPTION:
  interpret_as:
    - data metadata ontology
    - schema reasoning model
    - data quality model
    - provenance and lineage structure
    - semantic retrieval model
  operations:
    - identify data resources
    - resolve schemas
    - interpret fields and values
    - validate datatypes
    - resolve units and value domains
    - assess data quality
    - trace provenance
    - traverse version history
    - preserve uncertainty
  constraints:
    - do_not_invent_missing_values
    - do_not_treat_null_as_zero
    - do_not_equate_format_with_datatype
    - do_not_discard_provenance
    - preserve_source_data_and_version_context

query:
  input:
    - data_resource
    - identifier
    - field
    - datatype
    - value_constraint
    - format
    - quality_constraint
    - provenance_constraint
  resolve:
    1. identify DataResource
    2. retrieve DataMetadata
    3. resolve DataSchema
    4. resolve fields and datatypes
    5. apply value-domain constraints
    6. validate units where applicable
    7. evaluate quality
    8. trace provenance and versions
  output:
    - data_resource
    - schema
    - fields
    - format
    - quality
    - provenance
    - version
    - confidence

inference:
  IF Dataset consistently conforms to observed field structure
  THEN infer candidate DataSchema
  WITH confidence <= 0.80

  IF field values consistently match one datatype
  THEN infer candidate DataType
  WITH confidence <= 0.85

  IF resource B is produced by documented transformation of resource A
  THEN infer B DERIVED_FROM A
  WITH confidence = 0.95

output:
  ontology_entity: DataMetadata
  data_structure: machine_interpretable
  schema: explicit_where_available
  field_semantics: preserved
  quality: reportable
  provenance: traceable
  version_history: traversable
  uncertainty: explicit
  semantic_retrieval: enabled

end flow