Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
Authors: Bo Xiong, Mojtaba Nayyeri, Linhao Luo, Zihao Wang, Shirui Pan, Steffen Staab
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 19,646 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,528 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |