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..
Modeling Knowledge Graphs with Composite Reasoning
Authors: Wanyun Cui, Linqiu Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations demonstrate that mitigating the composition risk not only enhances the performance of TF-based models across all tested settings, but also surpass or is competitive with the state-of-the-art performance on two out of four benchmarks. |
| Researcher Affiliation | Academia | Wanyun Cui, Linqiu Zhang Shanghai University of Finance and Economics EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | Yes | Our code, data and supplementary material are available at https://github.com/zlq147/Compil E |
| Open Datasets | Yes | We use four datasets of different scales, including two larger datasets (FB15k-237 and WN18RR), and two smaller datastes (UMLS and Kinship). Our code, data and supplementary material are available at https://github.com/zlq147/Compil E |
| Dataset Splits | No | No specific dataset split information (percentages or counts) for a validation set was provided. The paper mentions evaluating on test sets but does not detail how validation sets were used or their specific splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) were mentioned in the paper. |
| Experiment Setup | No | No specific experimental setup details, such as hyperparameter values (e.g., learning rate, batch size, epochs) or optimizer settings, were provided in the main text of the paper. |