Pairwise Link Prediction Model for Out of Vocabulary Knowledge Base Entities
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
Real-world knowledge bases such as DBPedia, Yago, and Freebase contain sparse linkage connectivity, which poses a severe challenge to link prediction between entities. To cope with such data scarcity issues, recent models have focused on learning interactions between entity pairs by means of relations that exist between them. However promising, some relations are associated with very few tail entities or head entities, resulting in poor estimation of the relation interaction between entities. In this article, we break the sole dependency of modeling relation interactions between entity pairs by associating a triple with pairwise embeddings, i.e., distributed vector representations for pairs of word-based entities and relation of a triple. We capture the interactions that exist between pairwise embeddings by means of a Pairwise Factorization Model that employs a factorization machine with relation attention. This approach allows parameters for related interactions to be estimated efficiently, ensuring that the pairwise embeddings are discriminative, providing strong supervisory signals for the decoding task of link prediction. The Pairwise Factorization Model we propose exploits a neural bag-of-words model as the encoder, which effectively encodes word-based entities into distributed vector representations for the decoder. The proposed model is simple and enjoys efficiency and capability, showing superior link prediction performance over state-of-the-art complex models on benchmark datasets DBPedia50K and FB15K-237.
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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 | 0.003 |
| Open science | 0.001 | 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