Understanding and Improving Knowledge Graph Embedding for Entity Alignment
Authors: Lingbing Guo, Qiang Zhang, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically verify the effectiveness of Neo EA by a series of experiments. The main results on V1 datasets are shown in Table 1. |
| Researcher Affiliation | Collaboration | 1College of Computer Science and Technology, Zhejiang University 2ZJU-Hangzhou Global Scientific and Technological Innovation Center 3Alibaba-Zhejiang University Joint Reseach Institute of Frontier Technologies 4State Key Laboratory for Novel Software Technology, Nanjing University, China. |
| Pseudocode | Yes | Algorithm 1 Neo EA |
| Open Source Code | Yes | https://github.com/guolingbing/Neo EA |
| Open Datasets | Yes | We used the latest benchmark provided by Open EA (Sun et al., 2020c), which consists of four sub-datasets with two density settings. Specifically, D-W , D-Y denote DBpedia (Auer et al., 2007)-Wiki Data (Vrandeˇci c & Kr otzsch, 2014) , DBpedia YAGO (Fabian et al., 2007) , respectively. EN-DE and EN-FR denote two cross-lingual datasets, both of which are sampled from DBpedia. |
| Dataset Splits | Yes | Table 1: Results on V1 datasets (5-fold cross-validation). |
| Hardware Specification | Yes | We used a single TITAN RTX for training, and SEA (the fastest model) as the basic EEA model. |
| Software Dependencies | No | The paper mentions several models and frameworks like Open EA, TransE, ConvE, but does not provide specific version numbers for software dependencies used in their implementation. |
| Experiment Setup | No | We modified only the initialization of the original project and kept the optimal hyper-parameter settings in Open EA to ensure a fair comparison. This statement indicates that hyper-parameters were used, but they are not explicitly listed within the paper. |