Scalable Instance Reconstruction in Knowledge Bases via Relatedness Affiliated Embedding
Why this work is in the frame
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Bibliographic record
Abstract
The knowledge base (KB) completion problem is usually formulated as a link prediction problem. Such formulation is incapable of capturing certain application scenarios when the KB contains multi-fold relations. In this paper, we present a new formulation of KB completion, called instance reconstruction. Unlike its link-prediction counterpart, which has linear complexity in the size of the KB, this problem has its complexity behave as a high-degree polynomial. This presents a significant challenge in developing scalable instance reconstruction algorithms. In this paper, we present a novel knowledge embedding model (RAE) and build on it an instance reconstruction algorithm (SIR). The SIR algorithm utilizes schema-based filtering as well as "relatedness" filtering for complexity reduction. Here relatedness refers to the likelihood that two entities co-participate in a common instance, and the relatedness metric is learned from the RAE model. We show experimentally that SIR significantly reduces computation complexity without sacrificing reconstruction performance. The complexity reduction corresponds to reducing the KB size by 100 to 1000 folds.
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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 | 0.001 |
| 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