Relational logistic regression: the directed analog of Markov logic networks
Bibliographic record
Abstract
www-ai.cs.uni-dortmund.de/PERSONAL/kersting.html homes.soic.indiana.edu/natarasr / cs.ubc.ca/˜poole/ Relational logistic regression (RLR) was presented at the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR-2014). RLR is the di-rected analogue of Markov logic networks. Whereas Markov logic networks define distributions in terms of weighted for-mulae, RLR defines conditional probabilities in terms of weighted formulae. They agree for the supervised learning case when all variables except a query leaf variable are ob-served. However, they are quite different in representing dis-tributions. The KR-2014 paper defined the RLR formalism, defined canonical forms for RLR in terms of positive con-junctive (or disjunctive) formulae, indicated the class of con-ditional probability distributions that can and cannot be repre-sented by RLR, and defined many other aggregators in terms of RLR. In this paper, we summarize these results and com-pare RLR to Markov logic networks.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".