Semantic interoperability on blockchain by generating smart contracts based on knowledge graphs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health 3.0 enables decision-making to be based on longitudinal data from multiple institutions spanning the patient's healthcare journey. Blockchain smart contracts can act as neutral and trustworthy intermediaries to implement such decision-making. In this distributed healthcare setting, transmitted data are structured using standards, such as Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), for semantic interoperability. Hence, the smart contract will require interoperability with the domain standard. However, it will also have to implement a complex communication setup to work in a distributed environment (e.g., using oracles), and be developed using special-purpose blockchain languages (e.g., Solidity). To support these requirements, we propose the encoding of smart contract logic using a high-level semantic Knowledge Graph (KG), which uses concepts and relations from a domain standard and additionally lists distributed data requirements. We subsequently deploy this semantic KG on blockchain via a hybrid on-/off-chain code-generation approach. We applied our approach to generate smart contracts for three health insurance cases from Medicare. We evaluated the generated contracts in terms of correctness and execution cost (i.e., gas) on blockchain. Finally, we discuss the suitability of blockchain—and by extension, our approach—for a number of healthcare use cases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it