Contextual Parameter Generation for Knowledge Graph Link Prediction
Authors: George Stoica, Otilia Stretcu, Emmanouil Antonios Platanios, Tom Mitchell, Barnabás Póczos3000-3008
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 {gis, ostretcu, e.a.platanios, tom.mitchell, bapoczos}@cs.cmu.edu |
| 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. |