Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Authors: Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on multiple benchmark knowledge graphs show that the proposed Rotat E model is not only scalable, but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction. |
| Researcher Affiliation | Collaboration | Zhiqing Sun 1 , Zhi-Hong Deng1, Jian-Yun Nie3, Jian Tang2,4,5 1Peking University, China 2Mila-Quebec Institute for Learning Algorithms, Canada 3Universit e de Montr eal, Canada 4HEC Montr eal, Canada 5CIFAR AI Research Chair EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes of our paper are available online: https://github.com/Deep Graph Learning/ Knowledge Graph Embedding. |
| Open Datasets | Yes | We evaluate the Rotat E on four large knowledge graph benchmark datasets including FB15k (Bordes et al., 2013), WN18 (Bordes et al., 2013), FB15k-237 (Toutanova & Chen, 2015) and WN18RR (Dettmers et al., 2017). |
| Dataset Splits | Yes | Dataset #entity #relation #training #validation #test FB15k 14,951 1,345 483,142 50,000 59,071 WN18 40,943 18 141,442 5,000 5,000 FB15k-237 14,541 237 272,115 17,535 20,466 WN18RR 40,943 11 86,835 3,034 3,134 |
| Hardware Specification | No | The paper does not explicitly state the hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify the version numbers for any software dependencies (e.g., libraries, frameworks, or programming languages) used in the experiments. |
| Experiment Setup | Yes | Hyperparameter Settings. We use Adam (Kingma & Ba, 2014) as the optimizer and fine-tune the hyperparameters on the validation dataset. The ranges of the hyperparameters for the grid search are set as follows: embedding dimension5 k {125, 250, 500, 1000}, batch size b {512, 1024, 2048}, self-adversarial sampling temperature α {0.5, 1.0}, and fixed margin γ {3, 6, 9, 12, 18, 24, 30}. Both the real and imaginary parts of the entity embeddings are uniformly initialized, and the phases of the relation embeddings are uniformly initialized between 0 and 2π. |