Instance-based Learning for Knowledge Base Completion
Authors: Wanyun Cui, Xingran Chen
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various tasks confirmed the IBL model s effectiveness and interpretability. |
| Researcher Affiliation | Academia | Shanghai University of Finance and Economics1 University of Michigan2 |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1We release code at https://github.com/chenxran/Instance Based Learning |
| Open Datasets | Yes | Datasets: We select four typical KBC datasets for evaluation, including FB15k-237, WN18RR, Kinship, and UMLS 2. For Kinship and UMLS, we use the training/validation/test division in [17]. |
| Dataset Splits | Yes | For Kinship and UMLS, we use the training/validation/test division in [17]. |
| Hardware Specification | Yes | All experiments can be run on a single Nvidia Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions training techniques and refers to previous work but does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper states 'We illustrate the hyper-parameter search process in the Appendix,' which defers specific experimental setup details to supplementary material not included in the main text. |