BERT-INT:A BERT-based Interaction Model For Knowledge Graph Alignment
Authors: Xiaobin Tang, Jing Zhang, Bo Chen, Yang Yang, Hong Chen, Cuiping Li
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our model significantly outperforms the best state-of-the-art methods by 1.9-9.7% in terms of Hit Ratio@1 on the dataset DBP15K. |
| Researcher Affiliation | Academia | Xiaobin Tang1,2 , Jing Zhang1,2 , Bo Chen1,2 , Yang Yang3 , Hong Chen1,2 and Cuiping Li1,2 1Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education, Renmin University of China 2Information School, Renmin University of China 3Zhejiang University {txb, zhang-jing, bochen, chong, licuiping}@ruc.edu.cn, yangya@zju.edu.cn |
| Pseudocode | No | The paper describes the model architecture and steps in text and diagrams (Figure 2, Figure 3) but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Codes and datasets are online now3. 3https://github.com/kosugi11037/bert-int |
| Open Datasets | Yes | We evaluate our model on the widely used cross-lingual dataset DBP15K and the mono-lingual dataset DWY100K and use Hit Ratio@K (K=1,10) and MRR to evaluate (Cf. [Sun et al., 2018] for details). |
| Dataset Splits | No | We evaluate our model on the widely used cross-lingual dataset DBP15K and the mono-lingual dataset DWY100K and use Hit Ratio@K (K=1,10) and MRR to evaluate (Cf. [Sun et al., 2018] for details). The paper uses widely recognized datasets, but it does not explicitly state the specific training, validation, and test dataset splits (e.g., percentages or sample counts) within the text. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'pre-trained multi-lingual BERT' and links to its GitHub repository, but it does not specify concrete version numbers for BERT or other software dependencies like Python, PyTorch/TensorFlow, or specific libraries. |
| Experiment Setup | Yes | The dimension of the BERT CLS embedding is 768. We use a 300-dimension MLP in Eq.(1) and a 11 plus 1-dimension MLP in Eq.(5). The maximal number of neighbors and attributes are both set as 50. In Eq.(3), we use 20 semantic matching kernels, where µ is from 0.025 to 0.975 with interval 0.05 and all σ = 0.1, and use an exact matching kernel with µ = 1.0 and σ = 10 3. The number of the returned candidates by the basic BERT unit, i.e., κ is set as 50, as we find that 99% ground truth can be included in the top-50 candidates. The margin m in Eq.(2) for fine-tuning BERT is set as 3, and for training the interaction model is set as 1. |