Automating Entity Matching Model Development
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 seeks to answer one important but unexplored question for Entity Matching (EM): can we develop a good machine learning pipeline automatically for the EM task? If yes, to what extent the process can be automated? To answer this question, we find that a general-purpose AutoML tool cannot be directly applied to solve an EM problem, thus propose AutoML-EM, an automated model pipeline development solution tailored for EM. In reality, however, another bottleneck of EM problem is the insufficient labeled data. To mitigate this issue, active learning based solutions are widely adopted. Under this setting, we propose AutoML-EM-Active, investigating how to maximize the benefit of AutoML-EM with automatic data labeling. We provide fundamental insights into our solutions and conduct extensive experiments to examine their performance on benchmark datasets. The results suggest that AutoML-EM not only avoids human involvement in model development process but also reaches or exceeds the state-of-the-art EM performance, and AutoML-EM-Active improves the model performance under the active learning setting effectively.
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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