Ontology

TL;DR for operators Scientific-literature extraction is not only a question of whether an AI system can find the right facts. It also needs a representation capable of preserving the distinctions engineers will later use. Sadruddin et al. build four separate machine-validatable process schemas for atomic layer deposition (ALD) and atomic layer etching (ALE), divided again between experiments and simulations.1 The split is consequential: the schemas contain between 167 and 275 properties, while required-property counts range from just 3 in ALD simulation to 56 in ALE simulation. ...

Hospitals already have the raw material for better medical knowledge systems. It is sitting inside discharge summaries, nursing notes, radiology reports, ECG interpretations, and all the other clinical prose that makes electronic health records look deceptively “digital” while still behaving like a very expensive filing cabinet. The awkward part is that clinical notes are both valuable and dangerous. Valuable, because they contain granular observations that structured fields often miss. Dangerous, because they contain protected health information, idiosyncratic phrasing, and enough local context to make naïve automation look clever right up to the moment it quietly corrupts a downstream system. ...