Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Authors: Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations. |
| Researcher Affiliation | Academia | 1Institute of Computer Science and Technology, Peking University, China 2School of Computing and Communications, Lancaster University, U. K. {wyting, lxlisa, fengyansong, ruiyan, zhaodongyan}@pku.edu.cn, z.wang@lancaster.ac.uk |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We evaluate our approach on three large-scale cross-lingual datasets from DBP15K [Sun et al., 2017]. glove.840B.300d 1 http://nlp.stanford.edu/projects/glove/ |
| Dataset Splits | No | We use the same training/testing split with previous works [Sun et al., 2018], 30% for training and 70% for testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'glove.840B.300d' for word vectors but does not specify any software environments or library versions used for implementation. |
| Experiment Setup | Yes | The configuration we used is: β1 = 0.1, β2 = 0.3, and γ = 1.0. The dimensions of hidden representations in dual and primal attention layers are d = 300, d = 600, and d = 300. All dimensions of hidden representations in GCN layers are 300. The learning rate is set to 0.001 and we sample K = 125 negative pairs every 10 epochs. |