SparseMult: A Sparse Tensor Decomposition Model for Knowledge Graph Link Prediction
Bibliographic record
Abstract
ABSTRACT Knowledge graphs (KGs) have shown great power in many downstream natural language processing (NLP) tasks, such as recommendation system and question answering. Despite the large amount of knowledge facts in KGs, KGs still suffer from an issue of incompleteness, namely, lots of relations between entities are missing. Link prediction, also known as knowledge graph completion (KGC), aims to predict missing relations between entities. The models based on tensor decomposition, such as Rescal and DistMult, are promising to solve the link prediction task. However, previous Rescal model lacks the ability to scale to large KGs due to the large amount of parameters. DistMult simplifies Rescal by 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 SparseMult model, which is a novel tensor decomposition model based on sparse relation matrix. Specifically, we view KGs as 3D tensors and decompose them as entity vectors and relation matrices. To reduce the number of parameters in relation matrices, we represent each relation matrix as a sparse block diagonal matrix. Thus, the complexity of relation matrices grow linearly with the embedding size, making it able to scale up to large KGs. Moreover, we analyze the ability of modeling different relation patterns and show that our SparseMult is capable to model symmetry, antisymmetry, and inversion relations. We conduct extensive experiments on three widely used benchmark datasets FB15k‐237, WN18RR, and CCKS2021 KGs. Experimental results demonstrate that our SparseMult model outperforms most of the state‐of‐the‐art methods.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".