Field Name Matching

When integrating or cleaning data, it's common to encounter discrepancies in field names. These variations often occur due to differences in naming conventions across datasets. This situation calls for a strategy to match the field names in a dataset to a standard set of expected fields.

A simple yet effective strategy to match field names is to use a string similarity algorithm, such as "Jaccard Similarity". This algorithm calculates the similarity between two strings by dividing the size of the intersection of the sets of characters of the strings by the size of the union of these sets.

Let's illustrate this with a JavaScript example where we have a set of expected fields and a set of actual fields from a dataset. Our task is to match the actual fields with the expected fields.

function jaccardSimilarity(str1, str2) {
  let set1 = new Set(str1.split(""));
  let set2 = new Set(str2.split(""));
  let intersection = new Set([...set1].filter((x) => set2.has(x)));
  let union = new Set([...set1, ...set2]);
  return intersection.size / union.size;
}
let expectedFields = ["CustomerID", "ProductName", "Quantity", "Price"];
let actualFields = ["customer_ID", "product_name", "qty", "price"];
let mappedFields = actualFields.map((field) => {
  let maxSimilarity = 0;
  let bestMatch = "";
  expectedFields.forEach((expectedField) => {
    let similarity = jaccardSimilarity(field, expectedField);
    if (similarity > maxSimilarity) {
      maxSimilarity = similarity;
      bestMatch = expectedField;
    }
  });
  return { actualField: field, mappedField: bestMatch };
});

In this example, the function jaccardSimilarity() calculates the similarity score of two strings. For each field in the actual dataset, the algorithm identifies the best match from the set of expected fields.

[
  { actualField: "customer_ID", mappedField: "CustomerID" },
  { actualField: "product_name", mappedField: "ProductName" },
  { actualField: "qty", mappedField: "Quantity" },
  { actualField: "price", mappedField: "Price" },
];

• Complexity of Algorithms. If your data includes semantically similar but lexically different field names, you might need more sophisticated algorithms or even AI-driven solutions. For example, machine learning models trained on semantic similarity could potentially infer that "num" and "count" are similar as they both indicate a quantity.
• Cross-Language Mapping. In globalized business settings, you may encounter datasets with field names in different languages. Here, simple string similarity algorithms fall short. You might need to leverage cross-lingual mapping solutions, such as multilingual embeddings or translation services, before performing the field-matching process.
• Setting a Confidence Threshold. When working with automated field-matching solutions, it's crucial to set an appropriate confidence threshold. This threshold helps to minimize false positives, where unrelated fields are erroneously mapped due to coincidental string similarity.
• Human-in-the-Loop. Regardless of how advanced your field-matching algorithm is, it's always beneficial to have a human confirm the accuracy of field matches, correct false positives or negatives, and provide feedback to further refine the algorithm.

• Trimming Fields: Before comparing string similarity, it is often beneficial to trim leading and trailing whitespace to avoid mismatches due to extra spaces.
• Normalizing Cases: Converting all characters to the same case (either lower or upper) can greatly increase the accuracy of string similarity algorithms.
• Removing Special Characters: Depending on the dataset and the specific use case, it may also be beneficial to remove special characters prior to comparison.