End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion
Authors: Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, Bowen Zhou3060-3067
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and show about 10% relative improvement over the stateof-the-art Conv E in terms of HITS@1, HITS@3 and HITS@10. Table 3: Link prediction for FB15k-237, WN18RR and FB15k-237-Attr datasets. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA 2JD AI Research, Mountain View, CA, USA |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for our model and experiments is publicly available 1. 1https://github.com/JD-AI-Research-Silicon-Valley/SACN |
| Open Datasets | Yes | Three benchmark datasets (FB15k-237, WN18RR and FB15k-237-Attr) are utilized in this study to evaluate the performance of link prediction. FB15k-237. The FB15k-237 (Toutanova and Chen 2015) dataset... WN18RR. WN18RR (Dettmers et al. 2017) is created from WN18 (Bordes et al. 2013)... FB15k-237-Attr. We extract the attribute triples of entities in FB15k-237 from FB24k. FB24k (Lin, Liu, and Sun 2016) is built based on Freebase dataset. |
| Dataset Splits | Yes | Each dataset is split into three sets for: training, validation and testing, which is same with the setting of the original Conv E. Table 2: Statistics of datasets. Dataset... Train Edges... Val. Edges... Test Edges... |
| Hardware Specification | Yes | Our models are implemented by Py Torch and run on NVIDIA Tesla P40 Graphics Processing Units. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The hyperparameters in our Conv-Trans E and SACN models are determined by a grid search during the training. We manually specify the hyperparameter ranges: learning rate {0.01, 0.005, 0.003, 0.001}, dropout rate {0.0, 0.1, 0.2, 0.3, 0.4, 0.5}, embedding size {100, 200, 300}, number of kernels {50, 100, 200, 300}, and kernel size {2 1, 2 3, 2 5}. Here all the models use the WGCN with two layers. For different datasets, we have found that the following settings work well: for FB15k-237, set the dropout to 0.2, number of kernels to 100, learning rate to 0.003 and embedding size to 200 for SACN; for WN18RR dataset, set dropout to 0.2, number of kernels to 300, learning rate to 0.003, and embedding size to 200 for SACN. |