A knowledge identification framework for the engineering of ontologies in system composition processes
Bibliographic record
Abstract
Recent research has been focused on the creation of intelligent compositional systems that utilize ontologies as a knowledge base to facilitate the composition of new systems/workflows. Within this “ontology-driven” compositional systems field, experts have created knowledge representation models to satisfy requirements of their own domain rather than considering a general perspective. This paper proposes a knowledge identification framework to facilitate collaborative decision-making during knowledge requirement gathering to assist in the capture, merging, and mapping within an ontology engineering methodology. Five categories of knowledge (and a mapping of their relationships) are recognized as knowledge elements that should at least be considered in any representation model. A differentiation of syntactic and semantic knowledge, and a depiction of external influences on the composition process is also included. The paper concludes that while the presented framework does not guarantee an optimal ontological model, it does assist with the knowledge identification process for single or multiple stakeholders in ontology engineering for compositional systems.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".