I've been evaluating Iris.ai for a few weeks now, primarily for mapping literature in a project that sits at the intersection of materials science and computational biology. I've hit a consistent snag that's slowing down my workflow: handling interdisciplinary terms that carry completely different meanings.
For example, a term like "scaffold" means one thing in tissue engineering (a structure for cell growth) and something entirely different in software development or even in educational theory. Iris.ai's filters and context tools seem powerful, but I'm struggling to get clean results.
* How do you configure the workspace or queries to tell the engine which domain's definition you need?
* Does it rely purely on the seed documents you provide, or are there more advanced disambiguation settings?
* How does this compare to other tools you've used, like Semantic Scholar or even enterprise search platforms, in handling this specific challenge?
I'm curious about both the technical approach and the practical workflow. Are you adding these terms to a custom dictionary, or using the "exclude" functions heavily? Any examples from fields like "model" (statistical vs. fashion vs. engineering) or "agent" would be really helpful to understand the best practice here.