Temporal Knowledge Graph Reasoning with Historical Contrastive Learning
Authors: Yi Xu, Junjie Ou, Hui Xu, Luoyi Fu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed model on five benchmark graphs. The results demonstrate that CENET significantly outperforms all existing methods in most metrics, achieving at least 8.3% relative improvement of Hits@1 over previous state-of-the-art baselines on event-based datasets. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering Shanghai Jiao Tong University |
| Pseudocode | Yes | Algorithm 1: Learning algorithm of CENET |
| Open Source Code | Yes | All our datasets and codes are publicly available1. 1https://github.com/xyjigsaw/CENET |
| Open Datasets | Yes | All our datasets and codes are publicly available1. 1https://github.com/xyjigsaw/CENET |
| Dataset Splits | Yes | All datasets except ICEWS14 are split into training set (80%), validation set (10%), and testing set (10%). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | As to model configurations, we set the batch size to 1024, embedding dimension to 200, learning rate to 0.001, and use Adam optimizer. The training epoch for L is limited to 30, and the epoch for the second stage of contrastive learning is limited to 20. The value of hyperparameter α is set to 0.2, and λ is set to 2. |