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..
Contextual Parameter Generation for Knowledge Graph Link Prediction
Authors: George Stoica, Otilia Stretcu, Emmanouil Antonios Platanios, Tom Mitchell, Barnabás Póczos3000-3008
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our method on two existing link prediction methods, including the current state-of-the-art, resulting in significant performance gains and establishing a new state-of-the-art for this task. 5 Experiments In this section, we empirically evaluate the performance of Co PER on several established link-prediction datasets. |
| Researcher Affiliation | Academia | George Stoica,* Otilia Stretcu,* Emmanouil Antonios Platanios,* Tom M. Mitchell, Barnab as P oczos Carnegie Mellon University 5000 Forbes Ave, Pittsburgh, Pennsylvania 15213 EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | All supplementary material along with code to reproduce our experiments can be accessed at: https://github.com/otiliastr/coper. |
| Open Datasets | Yes | We adopt the following datasets used in prior literature: Unified Medical Language Systems (UMLS) (Kok and Domingos 2007), Alyawarra Kinship, WN18RR (Dettmers et al. 2018), FB15k-237 (Toutanova and Chen 2015), and NELL-995 (Xiong, Hoang, and Wang 2017). |
| Dataset Splits | Yes | To keep our train/validation/test dataset partitions consistent with those of prior literature and ensure fair comparisons, we use the published datasets from Das et al. (2018) and Lin, Socher, and Xiong (2018). |
| Hardware Specification | Yes | We conduct all our experiments on a single Nvidia Titan X GPU. |
| Software Dependencies | No | The paper states that the code is in a repository but does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) in the text. |
| Experiment Setup | Yes | We choose the dropout parameters by performing a grid search between [0,1] based on the validation set Hits@1. Regarding the parameter generation module, we perform experiments using both glinear and g MLP. For the MLP, we use a single hidden layer with a Re LU activation and chose the number of hidden units by also performing a grid search between. We train our models using the binary cross-entropy loss function. For each positive training example, we sample 10 negatives... and use a label smoothing factor of 0.1. |