Automated Comparative Table Generation for Facilitating Human Intervention in Multi-Entity Resolution
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
Entity resolution (ER), the process of identifying entities that refer to the same real-world object, has long been studied in the knowledge graph (KG) community, among many others. Humans, as a valuable source of background knowledge, are increasingly getting involved in this loop by crowdsourcing and active learning, where presenting condensed and easily-compared information is vital to help human intervene in an ER task. However, current methods for single entity or pairwise summarization cannot well support humans to observe and compare multiple entities simultaneously, which impairs the efficiency and accuracy of human intervention. In this paper, we propose an automated approach to select a few important properties and values for a set of entities, and assemble them by a comparative table. We formulate several optimization problems for generating an optimal comparative table according to intuitive goodness measures and various constraints. Our experiments on real-world datasets, comparison with related work and user study demonstrate the superior efficiency, precision and user satisfaction of our approach in multi-entity resolution (MER).
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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 | 0.005 | 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 | 0.001 |
| 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