Artificial intelligence (AI) is increasingly being used to improve DITA indexing in various ways:

Contextual Indexing: AI can analyze the context of content, enabling the creation of more context-aware index entries. It understands the relationships between terms and their significance in specific contexts.

Natural Language Processing (NLP): NLP techniques help AI understand the meaning and nuances of text, allowing for more accurate and relevant index entries.

Recommendation Systems: AI-powered recommendation systems can suggest potential index entries based on the content, reducing manual effort.

Faceted Search: AI-driven faceted search allows users to refine their search results through filters and categories, enhancing the indexing experience.

Example:

An AI-based DITA indexing tool utilizes NLP to understand the context of software documentation. It suggests index entries that are contextually accurate. When a user searches for a term, the system offers related terms and categories through faceted search, making it easier to find relevant content.

<!– Example of AI-enhanced DITA indexing –>

<index>
  <title>AI-Enhanced Index</title>
  <indexterm>
    <primary>Natural Language Processing</primary>
  </indexterm>
  <indexterm>
    <primary>Contextual Indexing</primary>
  </indexterm>
  <indexterm>
    <primary>Recommendation System</primary>
  </indexterm>
  <indexterm>
    <primary>Faceted Search</primary>
  </indexterm>
</index>

In this example, AI is employed to enhance DITA indexing with a focus on context, accuracy, and user experience.