Inductive Relation Prediction by BERT
Authors: Hanwen Zha, Zhiyu Chen, Xifeng Yan5923-5931
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | BERTRL outperforms the SOTAs in 15 out of 18 cases in both inductive and transductive settings. Meanwhile, it demonstrates strong generalization capability in few-shot learning and is explainable. ... Empirical experiments on inductive knowledge graph completion benchmarks demonstrate the superior performance of BERTRL in comparison with state-of-the-art baselines: It achieves an absolute increase of 6.3% and 5.3% in Hits@1 and MRR on average. |
| Researcher Affiliation | Academia | Hanwen Zha, Zhiyu Chen, Xifeng Yan University of California, Santa Barbara {hwzha, zhiyuchen, xyan}@cs.ucsb.edu |
| Pseudocode | No | The paper includes a pipeline diagram (Figure 1) but no formal pseudocode blocks or algorithms. |
| Open Source Code | Yes | The data and code can be found at https://github.com/zhw12/BERTRL. |
| Open Datasets | Yes | We evaluate our method on three benchmark datasets: WN18RR (Dettmers et al. 2018), FB15k-237 (Toutanova et al. 2015), and NELL-995 (Xiong, Hoang, and Wang 2017), using their inductive and transductive subsets introduced by Gra IL(Teru, Denis, and Hamilton 2020) 1https://github.com/kkteru/grail |
| Dataset Splits | No | The paper states that 'The best learning rate and training epoch are selected based on validation set.' and provides table statistics for 'train' and 'ind-test' subsets. However, it does not provide explicit numerical proportions (e.g., 80/10/10 split) or absolute counts for the validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | Both BERTRL and KG-BERT were implemented in Py Torch using Huggingface Transformers library (Wolf et al. 2020). However, specific version numbers for PyTorch or Huggingface Transformers are not provided. |
| Experiment Setup | Yes | Learning rate 5e5 is set for BERTRL and 2e-5 for KG-BERT, and training epoch is 2 and 5 respectively. We sample 10 negative triples in negative sampling, and 3 reasoning paths in path sampling, and keep increasing the size does not improve performance. |