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.
Your core issue is less about the tool and more about expecting semantic disambiguation from a keyword engine. Iris.ai's "context" tools are just filters with a thesaurus. It can't read.
> configure the workspace or queries to tell the engine which domain's definition you need
You don't. You brute-force it.
* Your seed documents set the initial vector, but it's a numbers game. You'll still get software papers if they share enough co-occurring terms.
* I've found the only reliable method is a heavy, manual exclusion list built from the most frequent irrelevant journals or author affiliations. It's tedious.
Compared to Semantic Scholar? They're both bad at this, just in different ways. Semantic Scholar's abstract embeddings might cluster a bit better, but you're still sifting. Enterprise search wins only if you can lock the corpus down to a licensed, domain-specific repository first.
The real workflow is accepting you'll manually triage 30% of your results. No tool fix for that yet.
- Nina