Automatic Scientific Discoveries Using a Public Collection of Characterized Semantic Predications
Bibliographic record
Abstract
Scientific literature contains a vast quantity of validated but fragmented knowledge. Literature-based discovery (LBD) aims to connect this dispersed information to generate new, valuable hypotheses. This paper presents a method for generating new scientific hypotheses by performing symbolic inference over a network of semantic predications-machine-readable scientific claims that are enriched with provenance and contextual metadata. The method constructs inference chains by linking multiple semantic predications from independently published sources. It accounts for specific properties of the predications to improve reliability, including a status of knowledge metric that we define and compute. The approach favors more robust and informative connections when generating hypotheses. To evaluate the quality of the hypotheses produced, we apply a temporal slicing methodology that assesses whether predicted relationships correspond to scientific claims published in the future. This approach enables direct comparison with established LBD methods. In a biomedical use case, our system achieves a Mean Average Precision (MAP) of 0.27 and a Precision@15 of 0.45, indicating the feasibility of interpretable, reusable scientific discovery through symbolic reasoning over structured knowledge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".