Mining Contextual Item Similarity without Concept Hierarchy
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
In the modern era, data is precious. Therefore, a huge amount of data is being generated every moment and data mining extracts insight from this data. Item similarity mining is a special domain of data mining that helps discover inherent and important characteristics of a dataset. It is a popular research problem with application in numerous domains. In this work, we propose a novel, symmetric, null-invariant measure of similarity that can evaluate contextual similarity between items, without any additional metadata. We also propose an optimal algorithm for calculating this measure. Moreover, as the optimal algorithm has comparatively high runtime complexity, we propose a heuristic algorithm which generates an approximate result without sacrificing much accuracy. This similarity can be used for mining localized associations and discovering object relationships in large datasets. The results obtained using the proposed measure in six real-life datasets confirm the measure’s effectiveness and versatility in data of varying nature.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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