Suggest semantic annotations for a dictionary

Searches external vocabularies to suggest IRIs for semantic gaps in the dictionary and package metadata. Measurement columns keep full I-ADOPT decomposition (term_iri, property_iri, entity_iri, unit_iri, constraint_iri), while selected non-measurement columns can receive lighter term_iri coverage when they are categorical or controlled low-cardinality attributes.

Usage

suggest_semantics(
  df,
  dict,
  sources = c("smn", "gcdfo", "ols", "nvs"),
  include_dwc = FALSE,
  max_per_role = 3,
  search_fn = find_terms,
  codes = NULL,
  table_meta = NULL,
  dataset_meta = NULL,
  llm_assess = FALSE,
  llm_provider = c("openai", "openrouter", "openai_compatible", "chapi"),
  llm_model = NULL,
  llm_api_key = NULL,
  llm_base_url = NULL,
  llm_top_n = 5L,
  llm_context_files = NULL,
  llm_context_text = NULL,
  llm_timeout_seconds = 60,
  llm_request_fn = NULL
)

Arguments

df

A data frame or tibble containing the data being documented, or a named list of data frames for multi-table workflows. When a named list is supplied, suggest_semantics() matches each dictionary row to the correct table via dict$table_id and uses that table's data as context.

dict

A dictionary tibble created by infer_dictionary() (may have incomplete semantic fields).

sources

Character vector of vocabulary sources to search. Options are "smn" (Salmon Domain Ontology via content negotiation), "gcdfo" (DFO-specific source), "ols" (Ontology Lookup Service), "nvs" (NERC Vocabulary Server), and "bioportal" (requires BIOPORTAL_APIKEY environment variable). Default is c("smn", "gcdfo", "ols", "nvs").

include_dwc

Logical; if TRUE, also attach DwC-DP export mappings (via suggest_dwc_mappings()) as a parallel attribute dwc_mappings. Default is FALSE to keep the UI simple for non-DwC users.

max_per_role

Maximum number of suggestions to keep per I-ADOPT role (variable, property, entity, unit, constraint) per column. Default is 3.

search_fn

Function used to search terms. Defaults to find_terms(). Can be replaced for testing or custom search strategies.

codes

Optional codes.csv-like tibble. When provided, suggestions are also generated for missing codes.csv$term_iri targets.

table_meta

Optional tables.csv-like tibble. When provided, suggestions are generated for missing tables.csv$observation_unit_iri.

dataset_meta

Optional dataset.csv-like tibble. When provided, suggestions are generated for missing dataset.csv$keywords as candidate semantic keywords (IRIs intended for keyword curation).

llm_assess

Logical; if TRUE, assess the top semantic candidates per target with an LLM after deterministic retrieval. When the first shortlist looks weak, the LLM may request at most one bounded alternate-query pass (1–2 plain-text search phrases) before a single reassessment. Default is FALSE.

llm_provider

LLM provider preset. One of "openai", "openrouter", "openai_compatible", or "chapi".

llm_model

Character model identifier. Required when llm_assess = TRUE unless supplied via METASALMON_LLM_MODEL. When llm_provider = "openrouter" and no model is supplied, the package defaults to "openrouter/free". Any valid OpenRouter model ID may be supplied here (for example "openai/gpt-5.4-mini"). When llm_provider = "chapi" and no model is supplied, the package defaults to "ollama2.mistral:7b" and also checks CHAPI_MODEL.

llm_api_key

Optional API key override. If omitted, provider-specific environment variables are used (OPENAI_API_KEY, OPENROUTER_API_KEY, CHAPI_API_KEY, or METASALMON_LLM_API_KEY).

llm_base_url

Optional base URL override for the OpenAI-compatible chat endpoint. Required for llm_provider = "openai_compatible" when not set via METASALMON_LLM_BASE_URL. For llm_provider = "chapi", the package defaults to https://chapi-dev.intra.azure.cloud.dfo-mpo.gc.ca/api and also checks CHAPI_BASE_URL.

llm_top_n

Maximum number of retrieved candidates to send to the LLM per target for each assessment round. Default is 5.

llm_context_files

Optional character vector of local context files (for example README/markdown notes, CSV dictionaries, HTML exports, DOCX files, source/notebook files such as .R, .Rmd, or .qmd, Excel workbooks, or PDF reports) used to provide extra domain context to the LLM. PDF support uses the optional pdftools package; Excel support uses the optional readxl package.

llm_context_text

Optional character vector of extra inline context snippets passed alongside llm_context_files.

llm_timeout_seconds

Timeout for each LLM request in seconds. chapi models matching gpt-oss are automatically given at least 120 seconds because the internal endpoint can be slow to warm up.

llm_request_fn

Advanced/test hook overriding the low-level OpenAI-compatible request function.

Value

The dictionary tibble (unchanged) with a semantic_suggestions attribute containing a tibble of suggested IRIs. The suggestions tibble starts with column_name, dictionary_role, table_id, and dataset_id so the original dictionary term is visible before the candidate match. It also includes target_scope, target_sdp_file, and target_sdp_field so users can see exactly where each accepted suggestion would land in the Salmon Data Package. Additional columns include search_query, target_query_basis, target_query_context, column_label, column_description, label, iri, source, ontology, definition, retrieval_query, and retrieval_pass. If the underlying search results include a score column, it is preserved for downstream filtering. For non-column targets, the tibble also includes explicit destination context (target_row_key, target_label, target_description, code_value, code_label, code_description) so table-, dataset-, and code-level rows are inspectable without extra joins. When llm_assess = TRUE, the suggestions also include llm_* review columns such as llm_decision, llm_confidence, llm_selected, llm_candidate_rank, and bounded exploration metadata, and the dictionary gains a parallel semantic_llm_assessments attribute with one row per assessed target.

Details

The function uses the column's label or description as the search query and returns suggestions as an attribute on the dictionary tibble. This allows you to review candidates before accepting them into your dictionary.

Column targets keep full I-ADOPT behavior for column_role == "measurement" rows. Non-measurement coverage is lighter: only missing term_iri values are considered, focused on categorical rows and controlled low-cardinality attribute rows inferred through codes.csv. Identifier and temporal columns are skipped by default. When codes, table_meta, or dataset_meta are supplied, additional target rows are generated for codes.csv, tables.csv, and dataset.csv respectively. Table-level observation-unit queries ignore review placeholders such as MISSING METADATA: and fall back to real table metadata context instead.

When llm_assess = TRUE, the LLM only judges deterministically retrieved candidates; it does not mint new IRIs. If the first shortlist looks weak, the model may suggest at most one bounded alternate-query round (1–2 plain-text queries), the package reruns deterministic retrieval, de-dupes the merged shortlist, and reassesses once. Local context files are read on disk, chunked, and lexically trimmed down before prompt assembly so large README/report/workbook files do not get dumped wholesale into the model call.

A term can legitimately appear more than once with different dictionary_role values (for example as both a variable and a property). In that case, match_type still describes lexical match quality, while target_sdp_field tells you where that suggestion would be written in the package. The output adds role_collision and role_collision_note so variable-vs-property collisions stay explicit and destination-aware.

After calling this function, access suggestions with:

suggestions <- attr(result, "semantic_suggestions")

Suggestions stay separate by default. Review them first, then use apply_semantic_suggestions() for an explicit opt-in merge, or copy values manually when you need finer control.

See also

find_terms() for direct vocabulary searches, infer_dictionary() for creating starter dictionaries, apply_semantic_suggestions() for explicitly filling selected IRI fields, validate_dictionary() for checking dictionary completeness.

Examples

if (FALSE) { # \dontrun{
# Create a starter dictionary
dict <- infer_dictionary(my_data, dataset_id = "example", table_id = "main")

# Get semantic suggestions for measurement columns
dict_with_suggestions <- suggest_semantics(my_data, dict)

# View the suggestions
suggestions <- attr(dict_with_suggestions, "semantic_suggestions")
print(suggestions)

# Filter suggestions for a specific column
spawner_suggestions <- suggestions[suggestions$column_name == "SPAWNER_COUNT", ]

# Explicitly apply the top suggestion for one column without overwriting
# any existing IRIs in the dictionary
dict <- apply_semantic_suggestions(dict_with_suggestions, columns = "SPAWNER_COUNT")
} # }