From Keywords to Questions: Why Taxonomy Still Matters in the Age of AI

Remember when searching meant typing three or four words into a box, scanning a page of links and trying again with slightly different words? If you knew what something was called, you had a decent shot at finding it. If you didn’t, you might spend ten minutes looking for a document that was right there all along, filed under a term nobody outside the department had ever used.

Taxonomy helped solve that problem. A taxonomy is an organized set of terms used to describe content. In traditional search, those terms give people ways to browse by topic or narrow results by audience, format or subject. A search system can also use synonyms to connect different words for the same thing. Someone who searches for “staff handbook” can find the “employee manual” without having to guess what the organization calls it.

Today, people can ask a search tool a full question. “What should our church do if a volunteer gets hurt during a community cleanup?” An artificial intelligence (AI)-assisted system can look for related concepts such as injury reporting, volunteer safety and incident forms, even if the documents never mention a cleanup. Depending on how the tool is built, it might retrieve relevant passages, suggest a form or summarize the steps with links to the source material.

That’s a big improvement for someone who needs an answer and has no idea what to type. It also shows why taxonomy still matters. A system might find several documents about injuries, including an employee policy, a volunteer incident form and a general safety checklist. They’re all about the right subject. They serve different purposes. Consistent labels for audience, topic and document type can help the system surface the volunteer form and the instructions that go with it.

That distinction matters even more when the interface gives you a single answer. With a list of links, you can often spot that one result is for employees and another is for volunteers. When a tool blends information from several sources into a paragraph, those differences are easier to miss. If the search system is designed to use taxonomy when retrieving sources, it has a better chance of pulling from documents that apply to the person asking.

The work behind the scenes is familiar. Listen to the words people actually use. Agree on what your categories mean. Apply them consistently. Revisit them when the organization changes or people start asking questions the old categories never anticipated.

Search has become more forgiving about how we ask. That’s welcome news for anyone who’s ever opened six tabs to find one form. Taxonomy helps with the other half of the job. It gives both people and machines a way to tell which of those six results actually belongs in the answer.

At the end of the day, content needs to be findable, and that happens with a strong, standards-based taxonomy. Data Harmony is our patented, award winning, AI suite that leverages explainable AI for efficient, innovative and precise semantic discovery of your new and emerging concepts, to help you find the information you need when you need it.

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Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.