When a Knowledge Graph Rejects Correct Information
TL;DR for operators A knowledge system can reject correct information and still report excellent accuracy. In a climate-science study, ontology filtering removed 11,050 extracted assertions, although 93.4% were judged textually correct. The complete pipeline ultimately achieved 98.96% evaluator-judged correctness, but retained only 23,327 of 39,964 initial assertions. The operational lesson is to separate three admission requirements: whether information fits the data model, whether its cited evidence can be located, and whether that evidence supports the assertion. Each check solves a different problem. Enterprise teams should measure factual correctness, provenance failures, and valid-claim rejection separately, especially when a conservative knowledge base may omit information that users need. ...