A hierarchy of independence assumptions for multi-relational Bayes net classifiers
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Bibliographic record
Abstract
Many databases store data in relational format, with different types of entities and information about their attributes and links between the entities. Link-based classification (LBC) is the problem of predicting the class attribute of a target entity given the attributes of entities linked to it. In this paper we propose a new relational Bayes net classifier method for LBC, which assumes that different links of an object are independently drawn from the same distribution, given attribute information from the linked tables. We show that this assumption allows very fast multi-relational Bayes net learning. We define three more independence assumptions for LBC to unify proposals from different researchers in a single novel hierarchy. Our proposed model is at the top and the wellknown multi-relational Naive Bayes classifier is at the bottom of this hierarchy. The model in each level of the hierarchy uses a new independence assumption in addition to the assumptions used in the higher levels. In experiments on four benchmark datasets, our proposed link independence model has the best predictive accuracy compared to the hierarchy models and a variety of relational classifiers.
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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 | 0.000 | 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