ER: Equivariance Regularizer for Knowledge Graph Completion
Authors: Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Qingming Huang5512-5520
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first introduce the experimental settings and show main results. Then we conduct some ablation experiments. |
| Researcher Affiliation | Academia | Zongsheng Cao 1,2, Qianqian Xu 3,*, Zhiyong Yang 4, Qingming Huang 3,4,5,6,* 1 State Key Laboratory of Information Security, Institute of Information Engineering, CAS, Beijing, China 2 School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China 3 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, CAS, Beijing, China 4 School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China 5 Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, China 6 Peng Cheng Laboratory, Shenzhen, China caozongsheng@iie.ac.cn, xuqianqian@ict.ac.cn, yangzhiyong21@ucas.ac.cn, qmhuang@ucas.ac.cn |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Appendix1. 1https://github.com/Lion-ZS/ER |
| Open Datasets | Yes | We conduct experiments on three widely used benchmarks, WN18RR (Dettmers et al. 2017), FB15K-237 (Dettmers et al. 2017) and YAGO3-10 (Mahdisoltani, Biega, and Suchanek 2013) |
| Dataset Splits | Yes | We take Adagrad (Duchi, Hazan, and Singer 2011) as the optimizer in the experiment, and use grid search based on the performance of the validation datasets to choose the best hyperparameters. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper mentions 'Adagrad' as an optimizer but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Specifically, we search learning rates in {0.5, 0.1, 0.05, 0.01, 0.005, 0.001}, and search regularization coefficients in {0.001, 0.005, 0.01, 0.05, 0.1, 0.5}. All models are trained for a maximum of 200 epochs. |