Search academic databases using semantic search (meaning-based) rather than exact keyword matching, which finds sources that address your concept even if they use different terminology. This is particularly powerful when you're working across disciplines or with emerging topics where standardized terminology hasn't solidified.
Semantic search is a method of finding information based on the meaning and intent behind a query rather than exact keyword matches, allowing AI-powered tools to surface relevant academic sources even when you do not know the precise terminology a field uses. Unlike typing keywords into a library database, semantic search understands conceptual relationships between ideas.
For students early in their research process, this is a game-changer because it closes the vocabulary gap between what you know and what scholars have written. AI tools that use semantic search help you find foundational papers, adjacent arguments, and cross-disciplinary sources that a keyword-only search would completely miss, giving your literature review depth from the start.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
Explore related journeys or tell Peri what you're working through.