Mining relational databases with multi-view learning
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
Most of today's structured data resides in relational databases where multiple relations are formed by foreign key joins. In recent years, the field of data mining has played a key role in helping humans analyze and explore large databases. Unfortunately, most methods only utilize "flat" data representations. Thus, to apply these single-table data mining techniques, we are forced to incur a computational penalty by first converting the data into this "flat" form. As a result of this transformation, the data not only loses its compact representation but the semantic information present in the relations are reduced or eliminated. In this paper, we describe a classification approach, which addresses this issue by operating directly on relational databases. The approach, called MVC (Multi-View Classification), is based on a multi-view learning framework. In this framework, the target concept is represented in different views and then independently learned using single-table data mining techniques. After constructing multiple classifiers for the target concept in each view, the learners are validated and combined by a meta-learning algorithm. Two methods are employed in the MVC approach, namely (1) target concept propagation and (2) multi-view learning. The propagation method constructs training sets directly from relational databases for use by the multi-view learners. The learning method employs traditional single-table mining techniques to mine data straight from a multi-relational database. Our experiments on benchmark real-world databases show that the MVC method achieves promising results in terms of overall accuracy obtained and run time, when compared with the FOIL and CrossMine learning methods.
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 | 0.001 |
| 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