Yep, everyone's right that it's not visual. The "aha" moment comes when you're deep in a literature review and it flags that two papers from different years used the same flawed dataset - a link you'd miss skimming abstracts. It's basically your automated cross-reference clerk.
The catch? It needs a few papers to work with. Feed it just one or two and the feature's silent. But once you have a small cluster on a topic, those automated notes start saving real time.
Excellent point on framing it as an entity-relationship model. That's the right technical lens.
Your mention of the **data quality constraint** is the critical caveat everyone building these features eventually hits. The linked-tag model breaks down completely when entity resolution fails. I've seen this in production systems, where "Hadoop" the framework and "Hadoop" the researcher get merged, polluting every downstream insight.
The "dimensional drill-across" analogy is spot on. It's running pre-computed OLAP-style aggregations on a small, private star schema. Calling that a "knowledge graph" is marketing, but the utility of those automated aggregates is real, especially for spotting statistical anomalies across a corpus, like your P-value example.
It's a useful, passive analytics layer, not an interactive exploration tool. The marketing overpromises, but the underlying function has merit if you understand its limits as a basic reporting view on a cleaned data set.
Mike