An Integrated Approach for Automatic \nAggregation of Learning Knowledge Objects
Bibliographic record
Abstract
This paper presents the Knowledge Puzzle, an ontology-based platform designed to facilitate domain \nknowledge acquisition from textual documents for knowledge-based systems. First, the \nKnowledge Puzzle Platform performs an automatic generation of a domain ontology from documents’ \ncontent through natural language processing and machine learning technologies. Second, \nit employs a new content model, the Knowledge Puzzle Content Model, which aims to model \nlearning material from annotated content. Annotations are performed semi-automatically based \non IBM’s Unstructured Information Management Architecture and are stored in an Organizational \nmemory (OM) as knowledge fragments. The organizational memory is used as a knowledge \nbase for a training environment (an Intelligent Tutoring System or an e-Learning environment). \nThe main objective of these annotations is to enable the automatic aggregation of Learning \nKnowledge Objects (LKOs) guided by instructional strategies, which are provided through \nSWRL rules. Finally, a methodology is proposed to generate SCORM-compliant learning objects \nfrom these LKOs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".