This is a valuable, next-gen capability, especially when an organization leverages federated search.
A full-text search is the ability to search all of the words within documents and presentations for the actual search terms used. This means learners can use more comprehensive search terms such as “key issues associated with learning project scope” for relevant search results down to the paragraph or presentation slide.
Adding Boolean capabilities to a full-text search engine can further refine results by recognizing conjunctive words such as “and” and “or.”Semantic search:
A common problem with legacy LMS search engines is their inability to understand what users actually mean. For instance, if learners search for “closing challenges,” do they mean challenges in closing a deal, or challenges in closing a window?
Semantic search corrects this issue by combining search technology with a tagging and taxonomy strategy. Basically, it constructs a scenario-based taxonomy or ontology that focuses on how content is used. This allows individuals to search content with selections that are specifically relevant to their business, job role or other context.
Sematic search leverages this ontology scheme, along with comprehensive user profiling and content tagging, to fully maximize the context and usefulness of search results. When coupled with federated search, it can be a powerful mechanism to provide learners with the on-demand knowledge they need to succeed in their day-to-day work.
All in all, providing robust, expedient search has become critically important to today’s learning organizations. Without the proper connections and technologies, companies can lose thousands of manpower hours, millions of dollars and substantial competitive gains.
Whether an enterprise learning organization has a legacy LMS or it’s building a new learning portal, it’s worth the time to investigate and invest in new search tools, such as federated, full-text and semantic search technologies.