Ensemble Semi-supervised Entity Alignment via Cycle-Teaching
Authors: Kexuan Xin, Zequn Sun, Wen Hua, Bing Liu, Wei Hu, Jianfeng Qu, Xiaofang Zhou4281-4289
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed cycle-teaching framework, which significantly outperforms the state-of-the-art models when the training data is insufficient and the new entity alignment has much noise. Tables 1 and 2 present the entity alignment results. Cyc TEA outperforms all baselines, and is 4% to 11% higher than the strongest baseline Boot EA on Hits@1. |
| Researcher Affiliation | Academia | 1The University of Queensland, Brisbane, QLD 4072, Australia 2State Key Laboratory for Novel Software Technology, Nanjing University, China 3Soochow University, Suzhou, Jiangsu 215006, China 4Hong Kong University of Science and Technology, Kowloon, Hong Kong |
| Pseudocode | No | The paper describes algorithmic steps in text but does not include structured pseudocode or algorithm blocks with formal labels like "Algorithm" or "Pseudocode." |
| Open Source Code | Yes | We will release our source code in Git Hub1. 1https://github.com/Jade XIN/Cyc TEA |
| Open Datasets | Yes | We use the benchmark dataset released at the Open EA library (Sun et al. 2020b) for evaluation, which follows the data distribution of real KGs. |
| Dataset Splits | Yes | We follow the dataset splits in Open EA, where 20% reference alignment is for training, 10% for validation and 70% for test. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions building the framework on the Open EA library but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The order arrangement parameter ϵ = 0.2. We follow their implementations settings used in Open EA for a fair comparison. |