PhysioMap: an ontology-grounded causal knowledge graph of human physiology

Computational physiology needs representations that connect traits across biological scales while distinguishing causal, constitutive, and mathematical dependencies. We present PhysioMap, an ontology-grounded knowledge base of contextualized physiological traits and precisely defined relation types. A versioned projection maps entailed ontology patterns to a typed causal knowledge graph that constrains quantitative structural causal models. Derivative signs provide a separate qualitative abstraction, which the PhysioMap solver uses to analyze steady-state responses in the presence of feedback. A stratified expert re- view across all relation types supported most sampled relations and isolated a minority for correction or further investigation. In a rare metabolic disease application, nearly all determinate predictions agreed with the HPO-derived reference before post-hoc review; after the discordant reference directions were excluded, all remaining determinate predictions agreed. Shortest signed paths produced directional errors, particularly on cases for which the PhysioMap solver did not determine a direction, indicating that its abstentions concentrated difficult cases. Abduction usually narrowed the candidate set but often did not identify a unique cause. PhysioMap therefore connects ontology-grounded physiological content to interventional prediction and abduction under incomplete quantitative knowledge. Because PhysioMap curation and the HPO-derived reference may share supporting literature, and because abduction used a closed candidate pool, these analyses do not constitute independent clinical validation.