Semantic depth and fragmentation in scientific data lakes - Claude

thoughts on this response "My revised best version after your rebuttal I would now frame it like this: Existing data-lake discovery methods have largely been evaluated in regimes where semantic structure is weak, implicit, or learned from schemas and values. Scientific R&D lakes create a different regime: many entities can be normalized to governed identifiers, making simple namespace fragmentation a baseline rather than the central problem. The harder problem begins after identity resolution, when datasets remain only conditionally comparable because modality, protocol, unit, version, provenance, and biological context determine what a shared entity means. The research challenge is not merely to combine raw data signals with curated metadata, but to adjudicate between asserted semantics and recovered semantics. Ontologies, identifiers, and metadata provide authoritative but incomplete commitments; learned representations and value-level evidence provide broad but noisy similarity clai

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• Rikard Saqe