Tagging

In the vast ocean of enterprise data, content without tags is invisible. Whether it is a PDF, a DITA topic or a video file, an untagged asset is difficult for search engines to index and nearly impossible for users to find. Tagging solves this by attaching descriptive metadata to content, acting as a digital index that tells machines exactly what the information represents.

There are two primary approaches to tagging: manual and automated. Manual tagging relies on authors to select keywords, which ensures high relevance but is often slow, inconsistent and prone to human error. Automated tagging, powered by Semantic AI and Natural Language Processing (NLP), analyzes the text and suggests or applies tags instantly. To be effective, tagging must be governed by a taxonomy – a controlled vocabulary of approved terms. When content is tagged against a taxonomy, it becomes structured and interoperable. This allows a website to dynamically display "related articles," enables a chatbot to retrieve the correct troubleshooting guide and allows a customer to filter products by color, size or compatibility.

At RWS, we believe that tagging should be intelligent, not administrative. We help organizations move beyond manual, error-prone tagging to Smart Tagging, powered by Tridion Semantic AI.

Our platform analyzes content as it is written and suggests relevant tags from a controlled taxonomy. This Human + Technology approach ensures efficiency (the AI does the heavy lifting) and accuracy (the human author verifies the context). By connecting structured content with semantic tags, Tridion creates a "knowledge fabric" that makes information machine-readable. This allows our clients to automate content delivery, power smarter chatbots and ensure that the right information reaches the right person at the right time.