Latent semantic indexing (LSI) is a natural language processing technique that analyzes relationships between a set of documents and the terms they contain. The result? Search engines can identify related terms for keywords by searching billions of pages and using the findings to evaluate the degree to which a single web page reflects truly comprehensive content on a topic.

The days are behind us when entering a keyword in the search engine then sifting through tons of results was okay with users. Time does not allow for that type of commitment these days. This is true for the casual Internet searcher and even more so for those searching for content in their own databases and platforms.

Organizing your data in a database is a key step in ultimately finding it again when the time comes. However, no amount of search engine optimization can beat a solid taxonomy in helping you find the data. Your data can provide intelligence through analytics and predicting trends, etc., but only if it is properly organized. This is where a proper taxonomy is critical.

Whether you already have a taxonomy or you need one developed, it is important to partner with professionals who have experience in taxonomy development. A key step in building a custom taxonomy is working closely with subject matter experts. This is an individual with a deep understanding of a particular process, topic, technology, science, or type of equipment. Subject matter experts have unique expertise to solve specific problems or help meet particular technical challenges and are the best resource for creating a taxonomy that accurately matches the depth and breadth of your data.

Searching and finding are different in ways that can make a substantial difference to a findability strategy. Searching requires a relevance engine to correlate a non-specific search string with useful resources. Relevance engines work best with large volumes of content and large numbers of query strings to profile. The most effective search strategy is probably going to be to put your content where Google can index it.

Finding, however, requires a specific vocabulary to match the specific find term with a specific known resource. Indexing data with a common vocabulary against a solid taxonomy will result in more finding than searching.

Today’s information management challenges call for unique solutions. It is important to remember the value of a solid taxonomy and its role in the search process. How the content is classified impacts the findability of your data. Access Innovations has extensive experience in constructing taxonomies for academic publishers, and can provide solutions that are ANSI compliant.

Melody K. Smith

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

Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.