# Handling Semantic Ambiguity with prelabelled Vectors

# Introduction

You are developing a family of R packages that extend tidy data workflows with richer semantic and provenance-aware capabilities. The work began from practical experience building tidyverse-based data pipelines and repeatedly encountering the same limitation: while tidy datasets are highly efficient and semantically clear within a given workflow, much of their meaning remains implicit and dependent on the contextual knowledge of their creator. Once exported, serialized, or transferred across environments, this contextual information is often lost. :contentReferenceoaicite:0

    library(fscontext)
    library(dataset)
    #>
    #> Attaching package: 'dataset'
    #> The following object is masked from 'package:dplyr':
    #>
    #>     as_tibble

The `dataset` package introduces semantically enriched vectors and data frames that preserve explicit metadata throughout the workflow lifecycle. However, fully formal semantic annotation is verbose and cognitively demanding. Constructing semantically complete RDF-compatible objects is appropriate only for mature stages of a workflow.

In practice, semantic stabilization is usually incremental. Observational data often arrive with partially inconsistent, incomplete, or ambiguous labels. Before a variable can mature into a formally defined vector created with `labelled::labelled()` or `dataset::defined()`, analysts typically perform several rounds of semantic harmonisation.

The `prelabelled` class supports this intermediate stage.

Unlike formally defined semantic vectors, `prelabelled` vectors tolerate:

- incomplete semantic mappings;
- unresolved observational values;
- mixed coding conventions;
- gradual semantic stabilization.

This vignette demonstrates how provisional semantic assertions can be incrementally stabilised while preserving the original observational evidence.

# A small ambiguous dataset

We begin with a small dataset containing country observations. The dataset is intentionally inconsistent: some observations use full country names, while others already use ISO 3166 alpha-2 country codes.

Such ambiguity is extremely common in operational analytical workflows, particularly when datasets are merged from multiple sources or manually curated over time.

    country_data_1 <- data.frame(
      country = c(
        "Andorra", "LI", "San Marino", "AD", "Liechtenstein"
      ),
      time = c(
        2020, 2020, 2020, 2021, 2021
      ),
      value = c(
        1.2, 2.4, 3.1, 1.3, 2.5
      )
    )

## Creating provisional semantic assertions

We now create a lightweight semantic mapping.

The goal is not yet to create a formally closed semantic vocabulary. Instead, we begin stabilising the semantics incrementally by mapping some observational values to candidate semantic assertions.

Values that are not explicitly mapped remain self-describing.

    country_map <- c(
      "Andorra" = "AD",
      "Liechtenstein" = "LI",
      "San Marino" = "SM"
    )

    country_data_1$country <-
      prelabel(
        country_data_1$country,
        labels = country_map
      )

## Inspecting the prelabelled vector

The resulting vector preserves the original observational values while attaching a provisional semantic vocabulary in the `"prelabel"` attribute.

    print(country_data_1$country)
    #> [1] "Andorra"       "LI"            "San Marino"    "AD"
    #> [5] "Liechtenstein"
    #> attr(,"prelabel")
    #>       Andorra Liechtenstein    San Marino            LI            AD
    #>          "AD"          "LI"          "SM"          "LI"          "AD"
    #> attr(,"class")
    #> [1] "prelabelled" "character"

This separation between:

- observational evidence;
- semantic interpretation;

is a central design principle of the `prelabelled` class.

The observational values remain unchanged, while semantic operationalisation may evolve iteratively over time.

## Semantic operationalisation

Using `as.character()` operationalises the semantic assertions into a semantically stabilised character vector.

    country_data_2 <- data.frame(
      country = as.character(country_data_1$country),
      time = country_data_1$time,
      value = country_data_1$value
    )

    country_data_2
    #>   country time value
    #> 1      AD 2020   1.2
    #> 2      LI 2020   2.4
    #> 3      SM 2020   3.1
    #> 4      AD 2021   1.3
    #> 5      LI 2021   2.5

Mapped observations are converted into their candidate semantic assertions, while unmatched values remain self-describing.

This allows analysts to gradually reduce semantic ambiguity without destroying the original observational evidence.

# A more ambiguous dataset

The next dataset contains a more difficult form of semantic ambiguity.

Some observations use ISO 3166 alpha-2 country codes, while others use ISO 3166 alpha-3 codes or full country names. Although the observations are semantically related, they do not yet form a stable closed vocabulary.

    country_data_3 <- data.frame(
      country = c(
        "AD", "AND", "LI", "LIE", "SMR", "San Marino"
      ),
      time = c(
        2020, 2020, 2020, 2021, 2021, 2021
      ),
      value = c(
        1, 2, 3, 4, 5, 6
      )
    )

# Incremental semantic stabilization

The `prelabelled` workflow does not require complete semantic resolution from the outset.

Instead, semantic stabilization can proceed incrementally:

- observational ambiguities become explicit;
- partial semantic mappings accumulate gradually;
- unresolved values remain operationally usable;
- semantic assertions become progressively more stable.

    country_map_3 <- c(
      "Andorra" = "AD",
      "Andorra" = "AND",
      "Liechtenstein" = "LI",
      "San Marino" = "SM",
      "San Marino" = "SMR"
    )

    prelabelled_country <- prelabel(
      country_data_3$country,
      labels = country_map_3
    )

This approach is particularly useful in exploratory analytical workflows, archival reconstruction, metadata harmonisation, and cross-dataset integration tasks.

    prelabelled_country
    #> [1] "AD"         "AND"        "LI"         "LIE"        "SMR"
    #> [6] "San Marino"
    #> attr(,"prelabel")
    #>       Andorra       Andorra Liechtenstein    San Marino    San Marino
    #>          "AD"         "AND"          "LI"          "SM"         "SMR"
    #>            AD           AND            LI           LIE           SMR
    #>          "AD"         "AND"          "LI"         "LIE"         "SMR"
    #> attr(,"class")
    #> [1] "prelabelled" "character"

## Semantic workspaces

While `as.character()` provides lightweight semantic coercion, which may be more useful after semantic stabilisation.

    as.character(prelabelled_country)
    #> [1] "AD"  "AND" "LI"  "LIE" "SMR" "SM"

The `as_character()` method creates a provenance-preserving semantic workspace.

    as_character(prelabelled_country)
    #> [1] "AD"  "AND" "LI"  "LIE" "SMR" "SM"
    #> attr(,"prelabel")
    #>       Andorra       Andorra Liechtenstein    San Marino    San Marino
    #>          "AD"         "AND"          "LI"          "SM"         "SMR"
    #>            AD           AND            LI           LIE           SMR
    #>          "AD"         "AND"          "LI"         "LIE"         "SMR"
    #> attr(,"original_values")
    #> [1] "AD"         "AND"        "LI"         "LIE"        "SMR"
    #> [6] "San Marino"
    #> attr(,"original_values")attr(,"prelabel")
    #>       Andorra       Andorra Liechtenstein    San Marino    San Marino
    #>          "AD"         "AND"          "LI"          "SM"         "SMR"
    #>            AD           AND            LI           LIE           SMR
    #>          "AD"         "AND"          "LI"         "LIE"         "SMR"

The resulting vector retains:

- the original observational values;
- the provisional semantic vocabulary;
- additional semantic attributes.

This allows analysts to continue semantic refinement workflows while preserving reversibility and provenance awareness.

## From provisional semantics to formally defined semantics

The goal of `prelabelled` vectors is not to replace formally defined semantic vectors.

Instead, they provide a lightweight preparatory stage for incremental semantic stabilization.

Once semantic ambiguity has been sufficiently reduced, `prelabelled` vectors may mature into formally defined semantic vectors created with `labelled::labelled()` or `dataset::defined()`.

In this sense, semantic enrichment becomes an iterative analytical workflow rather than a single terminal annotation step.