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
Join is a powerful operator that combines records from two or more tables, which is of fundamental importance in the field of relational database. However, traditional join processing mostly relies on string equality comparisons. Given the growing demand for ad-hoc data analysis, we have seen an increasing number of scenarios where the desired join relationship is not equi-join. For example, in a spreadsheet environment, a user may want to join one table with a subject column country-name, with another table with a subject column country-code. Traditional equi-join cannot handle such joins automatically, and the user typically has to manually find an intermediate mapping table in order to perform the desired join. We develop a SEMA-JOIN approach that is a first step toward allowing users to perform semantic join automatically, with a click of the button. Our main idea is to utilize a data-driven method that leverages a big table corpus with over 100 million tables to determine statistical correlation between cell values at both row-level and column-level. We use the intuition that the correct join mapping is the one that maximizes aggregate pairwise correlation, to formulate the join prediction problem as an optimization problem. We develop a linear program relaxation and a rounding argument to obtain a 2-approximation algorithm in polynomial time. Our evaluation using both public tables from the Web and proprietary Enterprise tables from a large company shows that the proposed approach can perform automatic semantic joins with high precision for a variety of common join scenarios.
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 | 0.005 | 0.002 |
| 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 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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