Auto-tagging

Auto-tagging helps organizations apply consistent metadata at scale, addressing one of the biggest challenges in content management: human error and inconsistency. Instead of relying on authors to manually select terms from a complex taxonomy – a process often prone to subjectivity or fatigue – auto-tagging uses Natural Language Processing (NLP) and Semantic AI to analyze the text. The system identifies key entities (such as products, regions or technical concepts) and themes, then recommends tags that match the defined taxonomy.

This automation transforms content from static text into intelligent data. By ensuring that every piece of content is tagged logically and consistently, auto-tagging enables powerful downstream capabilities. It powers faceted search, allowing users to filter results precisely. It supports dynamic content assembly, where a system can automatically pull relevant modules to build a personalized web page or document. Furthermore, it creates the structured data necessary for knowledge graphs, helping organizations visualize connections between disparate information assets. While authors typically retain the ability to accept or reject tags (a Human-in-the-Loop workflow), the heavy lifting of classification is handled by the machine, freeing up time for content creation.

RWS supports auto-tagging through Tridion Semantic AI, giving organizations a powerful foundation for intelligent content. Our platform doesn't just match keywords; it understands context. Automated tag recommendations combined with human validation create reliable metadata that strengthens governance, improves reuse and powers more dynamic digital experiences. This approach ensures that your content ecosystem is not just a repository, but a connected knowledge engine.