Modeling and querying possible repairs in duplicate detection
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
One of the most prominent data quality problems is the existence of duplicate records. Current duplicate elimination procedures usually produce one clean instance (repair) of the input data, by carefully choosing the parameters of the duplicate detection algorithms. Finding the right parameter settings can be hard, and in many cases, perfect settings do not exist. Furthermore, replacing the input dirty data with one possible clean instance may result in unrecoverable errors, for example, identification and merging of possible duplicate records in health care systems. In this paper, we treat duplicate detection procedures as data processing tasks with uncertain outcomes. We concentrate on a family of duplicate detection algorithms that are based on parameterized clustering. We propose a novel uncertainty model that compactly encodes the space of possible repairs corresponding to different parameter settings. We show how to efficiently support relational queries under our model, and to allow new types of queries on the set of possible repairs. We give an experimental study illustrating the scalability and the efficiency of our techniques in different configurations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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