Investigating Plausible Reasoning Over Knowledge Graphs for Semantics-Based Health Data Analytics
Bibliographic record
Abstract
Plausible reasoning reflects the "plasticity" element of human reasoning, which, by leveraging the semantics of relevant concepts, allows dealing with incomplete data during decision making. We propose the SEmantics-based Data ANalytics (SeDan) framework that integrates plausible reasoning with expressive, fine-grained biomedical ontologies. Using this framework, an unresolvable query can be rewritten to explore the semantic knowledge graph and infer new knowledge. While the gained insights may be plausible, i.e., not supported by crisp deductive reasoning, they may still aid complex medical decision making by recommending plausible solutions. In this paper, we investigate the efficiency of SeDan in a real-world medical setting to pose intelligent medical queries from BioASQ challenges over the MEDLINE database. Experimental results show that SeDan can expand the query answering coverage by resolving up to 45% of initially unresolvable queries. The correctness of the inferred answers, as well as the underlying plausible reasoning processes, was verified by a domain expert.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".