An Integrated Approach for Automatic \nAggregation of Learning Knowledge Objects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it