A Framework for Knowledge Models Transformation: A Step Towards Knowledge Integration and Warehousing
Bibliographic record
Abstract
An intelligent decision support system should based on a knowledge warehouse (KW). A KW gathers knowledge initially expressed in different formalisms and therefore heterogeneous. Consequently, the KW building process requires knowledge homogenisation. This paper deals with this main issue; it introduces a three-layer architecture for a KW; more precisely, it focuses on the first layer architecture called Knowledge Acquisition and Transformation. This layer aims to transform heterogeneous knowledge models into the MOT (Modeling with Object Types) semi-formal language [Paquette, G (2002). Knowledge and Skills Modeling: A Graphical Language for Designing and Learning. Sainte-Foy: University of Quebec Press (in French).] that we have selected as a pivot knowledge model. For this transformation step, first, we design four meta-models; one for MOT and one for each of the three explicit knowledge models, namely, decision tree, association rules and clustering. Secondly, we define 15 transformation rules that we formalise in ATL (Atlas Transformation Language). Finally, we exemplify the knowledge transformation in order to show its usefulness for the KW building process.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".