Cao Robot for Taiwanese/English Knowledge Graph Application
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 proposes a Content Attention Ontology (CAO) robot for constructing Taiwanese/English Knowledge Graphs (KGs) by prompting audio or texts to Large Language Models (LLMs), including TAIDE, Zephyr, and Llama 3.1. The collected data includes lecture videos from the IEEE WCCI 2024 in Japan and the 2024 National Language Development Forum in Taiwan, along with students' learning data from the 2024 Summer School on Taiwanese/English Human and Robot Co-Learning at Rende Elementary School (RDES). In addition, the fundamental concepts of Computational Intelligence (CI) and Quantum CI (QCI) learning were incorporated into the study. The generative KGs highlight important concepts, relations, and communities within the collected teaching and learning data. Additionally, we utilized data from subjects wearing braincomputer interface (BCI) devices while speaking Taiwanese/English to generate KGs. We also compared the differences in these KGs and analyzed the similarities between the transcribed texts of lectures and learners. In the future, we plan to expand the CAO robot to more validation fields across Taiwan, aiming to engage young students in speaking Taiwanese while concurrently enhancing their English language skills through interaction with the robot.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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