NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
Authors: Bo Xiong, Mojtaba Nayyeri, Linhao Luo, Zihao Wang, Shirui Pan, Steffen Staab
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results showcase Nest E s significant performance gains over current baselines in triple prediction and conditional link prediction. |
| Researcher Affiliation | Academia | 1University of Stuttgart, Stuttgart, Germany 2Monash University, Melbourne, Australia 3Griffith University, Queensland, Australia 4University of Southampton, Southampton, United Kingdom |
| Pseudocode | No | The paper describes mathematical operations and model components but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | The code and pre-trained models are open available at https://github.com/xiongbo010/Nest E. |
| Open Datasets | Yes | We utilize three benchmark KGs: FBH, FBHE, and DBHE, that contain nested facts and are constructed by (Chung and Whang 2023). |
| Dataset Splits | Yes | We split T and b T into training, validation, and test sets in an 8:1:1 ratio. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper states 'We implement the framework based on Open KE 4 and the code 5' but does not specify version numbers for Open KE or any other software dependencies. |
| Experiment Setup | Yes | The detailed hyperparameter settings can be found in the Appendix. |