SparseMult: A Tensor Decomposition model based on Sparse Relation Matrix
Why this work is in the frame
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Bibliographic record
Abstract
Knowledge graphs (KGs) provide rich knowledge for lots of downstream tasks, such as recommendation system and question answering. However, KGs suffer from an incompleteness issue, namely, lots of relations between entities are missing. Link prediction, also known as knowledge graph completion (KGC), aims to predict missing relationships between entities. The models based on tensor decomposition, such as Rescal and DistMult, are one of the most effective methods to solve the link prediction task. However, previous Rescal method lack the ability to scale to large KGs due to the large amount of parameters. DistMult simplifies Rescal using diagonal matrices to represent relations, while it suffers from the limitation of dealing with antisymmetric relations. To address these problems, in this paper, we propose a novel tensor decomposition model based on sparse relation matrix, which is named as SparseMult. We conduct extensive experiments on link prediction task and experimental results show that our SparseMult model outperforms most of the state-of-the-art methods.
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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 | 0.000 |
| 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