Knowledge Graph Completion by Intermediate Variables Regularization
Authors: Changyi Xiao, Yixin Cao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments to verify the effectiveness of our regularization technique as well as the reliability of our theoretical analysis. |
| Researcher Affiliation | Academia | Changyi Xiao, Yixin Cao School of Computer Science, Fudan University changyi_xiao@fudan.edu.cn, caoyixin2011@gmail.com |
| Pseudocode | Yes | Algorithm 1 A pseudocode for IVR |
| Open Source Code | Yes | The code is available at https://github.com/changyi7231/IVR. |
| Open Datasets | Yes | We evaluate the models on three KGC datasets, WN18RR [Dettmers et al., 2018], FB15k237 [Toutanova et al., 2015] and YAGO3-10 [Dettmers et al., 2018]. |
| Dataset Splits | Yes | We use the filtered MRR and Hits@N (H@N) [Bordes et al., 2013] as evaluation metrics and choose the hyper-parameters with the best filtered MRR on the validation set. |
| Hardware Specification | Yes | The time is the AMD Ryzen 7 4800U CPU running time on the test set. |
| Software Dependencies | No | We use Adagrad [Duchi et al., 2011] with learning rate 0.1 as the optimizer. While Adagrad is specified, no version numbers for this optimizer or other software libraries (e.g., Python, PyTorch/TensorFlow) are provided. |
| Experiment Setup | Yes | We use Adagrad [Duchi et al., 2011] with learning rate 0.1 as the optimizer. We set the batch size to 100 for WN18RR dataset and FB15k-237 dataset and 1000 for YAGO3-10 dataset. We train the models for 200 epochs. The settings for total embedding dimension D and number of parts P are shown in Table 5. The settings for power α and regularization coefficients λi are shown in Table 6. |