A FML-based hybrid reasoner combining fuzzy ontology and Mamdani inference
Bibliographic record
Abstract
Fuzzy ontologies have been employed to represent and reason over fuzzy information, which often occurs in real-world applications. Fuzzy inference systems (FIS) are well-known computational intelligence systems whose inferences can also be exploited in fuzzy ontology-based applications. Specifically, the combination of fuzzy ontologies and Mamdani-type FIS can provide inferences involving fuzzy rules and numerical property values, which can be considered in other fuzzy ontology reasoning tasks. In this sense, this paper proposes a hybrid reasoner combining fuzzy ontology and Mamdani inference to provide meaningful inferences that are not available to fuzzy ontology-based applications in an integrated way. Fuzzy rules are represented with Fuzzy Markup Language, providing an abstraction level with regard to the underlying FIS implementation. Some experiments are presented regarding a recommender system context, including a comparison with a fuzzy description logic reasoner in terms of fuzzy rule reasoning semantics and integration issues.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".