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Automatic term assignment
Automatic term assignment

Automatic term assignment

Automatic term assignment is the process of automatically mapping business terms to data assets and asset columns. Terms can automatically be assigned to data assets and asset columns as part of metadata enrichment.

You can assign business terms manually by editing the data asset properties in a project or a catalog, or when you work with enrichment results.

If automatic term assignment is configured as part of metadata enrichment, such assignments are generated by several methods. These methods also generate suggestions for terms to assign.

The terms are assigned based on the confidence level. Initially, these associations are represented as candidates which domain experts and stewards can review and assign manually. The confidence level for when a term is suggested or automatically assigned is determined by the project's enrichment settings. The default confidence level to be exceeded is 75% for term suggestions, and 90% for automatic assignment of candidate terms.

Only published business terms can be assigned.

Methods used to generate term assignments

The following methods are used to generate term assignments:

  • The linguistic name matching method bases its result on the similarity between the term and the name of the data asset or column. For example, a column CREDNUM might be associated with a term Credit Card Number because of the similarity between the two names.

  • The class-based assignment method generates assignments based on data classification. If a data class was selected for an asset column either as the result of column analysis or manually, and if this data class is linked to one or more business terms, these terms are suggested or assigned if they exceed the respective thresholds. The term confidence level is the same as the confidence of the data class the term is linked with. For example, a column COL1 classified as an email address with 90% confidence is likely to be assigned to the term E-mail Address if the data class and term are linked. Because there is no linguistic similarity between the name of the column and the term, the linguistic name matching method is not capable of making this association.

    To enable the class-based assignment method, it is important to review data class to term linkage before running term assignment because appropriate linkage is an important prerequisite for high-quality results.

    Note that business terms linked to the predefined data classes Code, Identifier, Date, Text, Indicator, Quantity, and Boolean are not considered for term assignment.

  • The machine learning (ML) method uses one supervised machine learning model per project to assign terms. The model is initially trained on the first use of the ML method in that project. It is trained with published business terms in the categories enabled for the project and any available term assignments on reviewed columns in the project. If no term assignments are available, the training focuses on linguistic similarity of words in names and descriptions of terms and data assets. Terms can be assigned based on that similarity. For every 20 columns that were marked reviewed since the last training, the model is retrained based on new manual and confirmed automatic assignments. Retraining also happens when new terms are published.

A project administrator can customize some settings for the term assignment methods. See Default enrichment settings.

How the overall confidence is computed

A method that associates a term with a data asset computes a confidence, which is a numeric value between a configurable minimum and 1. The minimum value is configured as percentage threshold for which the term must match by the setting of the suggestion threshold for term assignment.

The confidence for an assigned or suggested term is shown as a percentage value. This value represents the overall confidence, which is the maximum of these values:

  • The confidence value returned by linguistic name matching
  • The confidence value returned by class-based assignment
  • The confidence value returned by ML-based assignment

Example:

Assuming the methods return the following confidence values for a column ADDRESS and term Home Address:

Linguistic name matching: 0.5
Class-based assignment: 0.4
ML-based assignment: 0.3

The overall confidence is 0.5 because it's the highest value returned by a method.

Publishing term assignments

When you publish the enrichment results, term assignments, whether manual or automatic, are available in the catalog and in all projects that contain a given data asset. Term suggestions are not published.

When you remove a published term assignment, all projects that contain the data asset are affected. While you work within the enrichment results, the changes are internal to the project. However, when you publish the changes, the term is removed from the asset in all projects it is contained in. Before you remove a published assignment, make sure that it wasn't added on purpose by other users. {

How new analysis results update existing term assignments

When you rerun an enrichment, a new analysis result updates term assignments as follows:

  • Existing suggested terms are deleted and replaced with the new suggested terms.
  • Existing automatic assignments are deleted and replaced with new automatic assignments.
  • Existing rejected terms and manual assignments are left untouched.

Learn more

Parent topic: Metadata enrichment results