An Ontological Model for Artificial Reasoning
Bibliographic record
Abstract
Representing knowledge in a language that is both understandable by humans and easily exploitable by machines remains the subject of several research studies. Domain ontologies are recognized as an efficient way to describe knowledge through concepts and relations in several domains of expertise while remaining shareable and reusable. This paper aims to propose an approach for “artificial reasoning” that we consider as a foundational pillar for “Artificial Intelligence.” Our particular interest in this work is on how to design systems that can use human knowledge to process and solve the complex problem of diagnosis, given the required expertise in a specific domain of knowledge. The approach we present in this paper is based on using properties of ontologies, by representing expert knowledge through a graph reasoning model, to formalize the diagnosis process using an ontology-based model. We first describe our proposal on how to represent expert knowledge in a general way before focusing on the diagnosis problem. Finally, we apply the whole process to the specific domain of cardiology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".