Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
Authors: Hidetaka Kamigaito, Katsuhiko Hayashi
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical analysis on the FB15k-237, WN18RR, and YAGO3-10 datasets showed that the results of actually trained models agree with our theoretical findings. |
| Researcher Affiliation | Academia | 1Nara Institute of Science and Technology (NAIST), Nara, Japan 2Hokkaido University, Hokkaido, Japan. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/kamigaito/icml2022. |
| Open Datasets | Yes | We used FB15k-237 (Toutanova & Chen, 2015), WN18RR, and YAGO3-10 (Dettmers et al., 2018) as the datasets. |
| Dataset Splits | Yes | Table 1: Statistics for each dataset. ... FB15k-237 ... Train 272,115 Valid 17,535 Test 20,466 ... WN18RR ... Train 86,835 Valid 3,034 Test 3,134 ... YAGO3-10 ... Train 1,079,040 Valid 4,978 Test 4,982 |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. It only mentions using Adam as an optimizer. |
| Software Dependencies | No | The paper mentions using Adam (Kingma & Ba, 2015) as an optimizer and refers to implementations from previous works, but it does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | Settings: ... We chose margin term γ from {0.0, 3.0, 6.0, 9.0, 9.58} on FB15k-237 and {0.0, 2.0, 4.0, 6.0, 10.62} on WN18RR for each initial learning rate in {e-2, e-3, 5e-5}. ... We varied the number of negative samples ν from {32, 64, 128, 256, 512} on FB15k-237 and {64, 128, 256, 512, 1024} on WN18RR for each initial learning rate in {e-2, e-3, 5e-5}. ... Table 6: Hyperparameters for each model. Batch denotes the batch size, Dim denotes the hidden dimension size, α denotes the temperature parameter for SANS, and Step denotes the max steps in training. ... Table 7: Hyperparameters for each model in 4.5. LR denotes the learning rate. |