Enhancing Large-Scale Entity Alignment with Critical Structure and High-Quality Context
Bibliographic record
Abstract
Entity Alignment (EA) aims to identify equivalent entities across multiple Knowledge Graphs (KGs). However, when applied to larger-scale KGs, most existing EA approaches suffer from the scalability issue due to excessive GPU memory and time consumption. To mitigate this, recent advances have introduced the Large-scale EA (LsEA) task, which divides large-scale KG pairs into smaller sub-graph pairs. Despite their promising results, several notable challenges remain, preventing these advances from achieving optimal performance: 1) How to effectively utilize critical structures when generating sub-tasks? 2) How to supplement high-quality context to enhance LsEA performance? 3) How to address scenarios without alignment seeds? To tackle these challenges, we propose a novel method called ELsEA. It comprises three main components: (1) Source and Target Graph Partition, using a Metis-based weighted partitioner and a counter-part candidate generator to partition source and target graphs respectively, aiming to utilize critical structures effectively; (2) Supplement High-quality Context, which utilizes a value-based informativeness-evaluation module and a neighbor enrichment module to assess each entity's informativeness effectively, then supplement high-quality context based on this informativeness; and (3) Seed-free Setup, introducing a mixed-info pseudo-seed generation strategy to mitigate name bias, generating accurate pseudo-seeds when alignment seeds are unavailable. Extensive experiments demonstrate that ELsEA outperforms state-of-the-art baselines. The code of ELsEA is available online11https://githuh.com/wx-qzhou/ELsEA.git.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".