docs: Add documentation for value matching methods

docs/source/index.rst

@@ -29,3 +29,4 @@ You can find the source code in our `GitHub repository <https://github.com/VIDA-
 
    api
    schema-matching
+   value-matching

docs/source/value-matching.rst

@@ -0,0 +1,39 @@
+Value Matching Methods
+======================
+
+This page provides an overview of all value matching methods available in the `bdikit` library.
+Some methods reuse the implementation of other libraries such as `PolyFuzz <https://maartengr.github.io/PolyFuzz/>`_ (e.g, `embedding` and `tfidf`) while others are implemented originally for bdikit (e.g., `gpt`).
+To see how to use these methods, please refer to the documentation of :py:func:`~bdikit.api.match_values()` in the :py:mod:`~bdikit.api` module.
+
+.. ``bdikit module <api>`.
+
+
+
+.. list-table:: bdikit methods
+    :header-rows: 1
+
+    * - Method
+      - Class
+      - Description
+    * - ``gpt``
+      - :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.GPTValueMatcher`
+      - | Leverages a large language model (GPT-4) to identify and select the most accurate value matches.
+
+.. list-table:: Methods from other libraries
+    :header-rows: 1
+
+    * - Method
+      - Class
+      - Description
+    * - ``tfidf``
+      - :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.TFIDFValueMatcher`
+      - | Employs a character-based n-gram TF-IDF approach to approximate edit distance by capturing the frequency and contextual importance of n-gram patterns within strings. This method leverages the Term Frequency-Inverse Document Frequency (TF-IDF) weighting to quantify the similarity between strings based on their shared n-gram features.
+    * - ``edit_distance``
+      - :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.EditDistanceValueMatcher`
+      - | Uses the edit distance between lists of strings using a customizable scorer that supports various distance and similarity metrics.
+    * - ``embedding``
+      - :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.EmbeddingValueMatcher`
+      - | A value-matching algorithm that leverages the cosine similarity of value embeddings for precise comparisons. By default, it utilizes the `bert-base-multilingual-cased` model to generate contextualized embeddings, enabling effective multilingual matching.​.
+    * - ``fasttext``
+      - :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.FastTextValueMatcher`
+      - | This method uses the cosine similarity of FastText embeddings to accurately compare and align values, capturing both semantic and subword-level similarities..