Similarity Definition Form

  • Release version: Zurich
  • Updated July 31, 2025
  • 2 minutes to read
    • Summarize
    Summarized using AI
    This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.

    Summary of Similarity Definition Form

    The Similarity Definition Form in Compliance Workspace enables ServiceNow customers to create similarity definitions for regulatory compliance mapping. This functionality is designed to help map and recommend relevant citations and issues by analyzing and comparing data records, improving the accuracy of compliance management.

    Show full answer Show less

    Key Features

    • Label and Name: Define a unique name for the similarity solution. The system auto-generates the Name based on the Label for consistency.
    • Word Corpus: Select an existing word corpus related to your use case, such as the “Word Corpus for Issue Assignment.” Note that this corpus supports up to 300,000 records.
    • Table: Specifies the dataset used for training and prediction. For regulatory compliance mapping, the table is preset to sncompliancecitation and should not be modified.
    • Fields: Choose relevant fields from the Table to improve citation recommendation accuracy. Common fields include Name, Document Name, Document Description, and Description. These can be customized based on your data to enhance prediction quality.
    • Test Table and Test Fields: The Test Table is preset to sngrcregchangeregulatoryfeed and Test Fields (such as Title and Description) are used as inputs during prediction. These fields can be adjusted to better fit your dataset.
    • Filter: Typically left empty for this solution definition.
    • Processing Language: Select the dominant language of your dataset (default is English). This setting affects language-specific processing steps like tokenization, stop word removal, and stemming to improve similarity matching.
    • Stopwords: Automatically populated based on the selected processing language, with the option to add custom stopword lists to refine text analysis.
    • Training Frequency: Defines how often the solution retrains on the dataset, with options up to every 180 days.
    • Update Frequency: Determines how often the solution refreshes data to retrieve similarity results, supporting frequencies as frequent as every 15 minutes to capture new citations promptly.

    Practical Use and Benefits

    By configuring the Similarity Definition Form correctly, ServiceNow customers can automate the mapping of regulatory citations to relevant compliance issues, enhancing the efficiency and accuracy of regulatory compliance processes. The form’s built-in limitations and preset tables ensure solutions are aligned with best practices while allowing field and language customization to fit specific organizational needs. Regular training and data refresh cycles maintain the relevance of similarity recommendations, supporting dynamic compliance environments.

    Use the Similarity Definition Form form in Compliance Workspace to create a similarity definition for regulatory compliance mapping.

    Similarity Definition form

    For a description of the field values, see the following table.
    Table 1. Similarity Definition form
    Field Description
    Label Unique name for the similarity solution.
    Name Name of the similarity solution. This field is automatically set to the system-assigned name that is the most similar to your value for the Label field.
    Word Corpus Existing word corpus that is relevant to your solution. For this use case, select the Word Corpus for Issue Assignment word corpus.
    Note:
    For word corpora in similarity solutions, the number of records according to table is limited to 300,000.
    Table Table that contains the records that you want to train against and to predict for. When you assign a table value, a link appears in the form. The link shows the number of records that match your current conditions.

    For this use case, the field is automatically set to the [sn_compliance_citation] table. Don’t modify this field for this solution definition.
    Fields Field types that are likely to help in recommending the citations. You can select the columns from the table in the Table field so that their data helps in predicting the citations more accurately. In this use case, the Name, the Document Name, Document Description, and Description fields are selected. These fields are the field types that contain the citation records that you want to recommend.
    Note:
    You can modify the fields selected here if there are other non-empty important fields on the issue record in your database, such that these fields can help in finding out similar citations for mapping to the regulatory alerts.
    Test Table Table that contains the citations that you want to predict for. For this use case, the field is automatically set to the Issue [sn_grc_reg_change_regulatory_feed] table.
    Note:
    The number of records which the Similarity window can retrieve is limited to 10. This field must not be modified for this solution definition.
    Test Fields Fields which are used as input during prediction. In this use case, select Title and Description.
    Note:
    You can modify the fields selected here if there are other non-empty important fields on the issue record in your database, such that these fields can help in finding out similar issues for predicting the issue owners.
    Filter Leave this field empty.
    Processing Language Dominant language of the dataset that you are training on the solution definition. If the dataset language is English, choose English.
    By default, English processing is applied to all datasets. For example, if you select English, the system processes the data in both English and Italian.
    Note:
    The term processing indicates some of the language-specific steps that are used as part of training a solution. These steps include tokenizing words, removing stop words, and stemming.
    Stopwords List of stopwords. When you select your processing language, the system automatically adds a Stopwords list that uses the same language. For example, if your processing language is English, the Default English Stopwords option appears. The Default English Stopwords list also appears in your selection. You can add your own custom list of stopwords.
    Training Frequency Frequency of training. The retraining option can range is 180 days.
    Update Frequency Frequency of how often you want to refresh the data that you use to retrieve your similarity results.

    For example, new citations typically occur frequently throughout the day. If you have new citations, you may want to select an update frequency of Every 15 minutes. This frequency can increase the likelihood that new citations are included in the refresh.