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Render GitHub-ready ontology term request payloads

render_ontology_term_request.Rd

Convert gap candidates into request payload rows (title/body) suitable for creating GitHub issues against the Salmon Domain Ontology repository by default.

Usage

render_ontology_term_request(
  gaps,
  scope = c("auto", "smn", "profile"),
  ask = interactive(),
  profile_name = NULL,
  scope_overrides = NULL,
  issue_labels = NULL,
  term_request_template = .term_request_default_template,
  ontology_repo = "salmon-data-mobilization/salmon-domain-ontology"
)

Arguments

gaps

Output from detect_semantic_term_gaps().

scope

One of "auto", "smn", or "profile".

  • "auto": honor placement_recommendation and ask for uncertainty

  • "smn": route all requests to shared SMN

  • "profile": route all requests to a profile

ask

If TRUE, unresolved rows are asked interactively.

profile_name

If routing to profiles, provide a default profile name.

scope_overrides

Optional per-row scope overrides ("smn", "profile", "skip"). Useful in non-interactive pipelines.

issue_labels

Optional labels to include on created GitHub issues.

term_request_template

URL for the target issue template.

ontology_repo

Repository slug to target when submitting issues.

Value

A tibble with one row per rendered request payload. Rows with request_scope == "skip" are retained and can be filtered before submission.

Details

For interactive workflows this function can prompt users row-by-row for whether a gap should be requested as a shared SMN term, a profile-specific term, or skipped.

See also

detect_semantic_term_gaps(), submit_term_request_issues(), validate_semantics()

Examples

gap <- dplyr::tibble(
  dataset_id = "d1",
  table_id = "t1",
  column_name = "run_id",
  code_value = NA_character_,
  target_scope = "column",
  target_sdp_file = "column_dictionary.csv",
  target_sdp_field = "term_iri",
  target_row_key = "run_id",
  dictionary_role = "variable",
  search_query = "run id",
  column_label = "Run ID",
  column_description = "Dataset-specific run identifier",
  top_non_smn_source = "gbif",
  top_non_smn_label = "Run event id",
  top_non_smn_iri = NA_character_,
  top_non_smn_ontology = NA_character_,
  top_non_smn_match_type = "label",
  top_non_smn_score = 0.9,
  candidate_count = 2,
  non_smn_sources = "gbif, worms",
  placement_recommendation = "profile",
  placement_confidence = 0.82,
  placement_rationale = "Contains internal identifier patterns."
)

render_ontology_term_request(
  gap,
  scope = "auto",
  ask = FALSE,
  profile_name = "pacific-monitoring"
)
#> # A tibble: 1 × 29
#>   dataset_id table_id column_name code_value target_scope target_sdp_file      
#>   <chr>      <chr>    <chr>       <chr>      <chr>        <chr>                
#> 1 d1         t1       run_id      NA         column       column_dictionary.csv
#> # ℹ 23 more variables: target_sdp_field <chr>, target_row_key <chr>,
#> #   dictionary_role <chr>, search_query <chr>, column_label <chr>,
#> #   column_description <chr>, top_non_smn_source <chr>,
#> #   top_non_smn_label <chr>, top_non_smn_iri <chr>, top_non_smn_ontology <chr>,
#> #   top_non_smn_match_type <chr>, top_non_smn_score <dbl>,
#> #   candidate_count <dbl>, non_smn_sources <chr>,
#> #   placement_recommendation <chr>, placement_confidence <dbl>, …