Record Fusion via Inference and Data Augmentation
Bibliographic record
Abstract
We introduce a learning framework for the problem of unifying conflicting data in multiple records referring to the same entity—we call this problem “record fusion.” Record fusion generalizes two known problems: “data fusion” and “golden record.” Our approach expresses record fusion as a learning problem over probabilistic models. In contrast to prior approaches, our method achieves high performance with or without the records source information and outperforms state-of-the-art baselines. Furthermore, we show how our learned fusion model can solve the problem of scarcity of training data. On multiple datasets, we show that our framework fuses records with an average precision of ∼98% when source information is available and ∼94% without source information across a diverse array of datasets. We compare our approach to a comprehensive collection of data fusion and entity consolidation methods, ranging from source information–related methods to approaches that do not need any source information. We show that our approach can achieve an average improvement of ∼20/∼45 precision points with/without source information. Our data augmentation method improves previous approaches an average of ∼10 precision points.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".