AI engines can analyze content and group similar content together. For instance, a Social Security number and its value.Ī third scenario for automated content tagging is grouping.
Sometimes, these are single keywords that a user adds to a list of relevant terms, but they can be sets of paired values, such as pairing a custom property name with its value. Extracting information from content, such as an article, and populating metadata improves the findability of content. The greater challenge for businesses is performing the same task for free-form content. That approach works well with case management scenarios, where AI tagging engines routinely process forms. This leads to quick startup cycles and ROI for the software. A longstanding feature of scanning software, tagging engines can scan incoming forms of various content types and extract key metadata with minimal training of the AI engine. The easiest form of automated content tagging is the extraction of form data.
The concept is simple instead of forcing business users to add metadata to content, an AI engine tags every piece of content. Content should include specific - and useful - metadata, such as document type, project name, effective date and other contextually relevant information.Īutomated content tagging is one approach to solving the challenge of finding the right content. Businesses must identify content by more than the basic metadata fields, such as author and title.
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