Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Authors: Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the proposed methods on two cross-lingual tasks: cross-lingual entity matching, and triple-wise alignment verification. We also conduct experiments on two monolingual tasks. |
| Researcher Affiliation | Academia | Muhao Chen1, Yingtao Tian2, Mohan Yang1, Carlo Zaniolo1 Department of Computer Science, UCLA1 Department of Computer Science, Stony Brook University2 {muhaochen, yang, zaniolo}@cs.ucla.edu; yittian@cs.stonybrook.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Experimental results on the trilingual data sets WK3l are reported in this section. WK3l contains English (En), French (Fr), and German (De) knowledge graphs under DBpedia s dbo:Person domain... Meanwhile, we derive another trilingual data set CN3l from Concept Net [Speer and Havasi, 2013]. |
| Dataset Splits | No | For each language version, 10% triples are selected as the test set, and the remaining becomes the training set. We use 10-fold cross-validation on these cases to train and evaluate the classifier. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for reproducing the experiments. |
| Experiment Setup | Yes | For training, we select the learning rate λ among {0.001, 0.01, 0.1}, α among {1, 2.5, 5, 7.5}, l1 or l2 norm in loss functions, and dimensionality k among {50, 75, 100, 125}. The best configuration on WK3l-15k is λ = 0.01, α = 5, k = 75, l1 norm for Var1, Var2, LM, and CCA, l2 norm for other variants and OT. While the best configuration on WK3l120k is λ = 0.01, α = 5, k = 100, and l2 norm for all models. The training on both data sets takes 400 epochs. We initialize vectors by drawing from a uniform distribution on the unit spherical surface, and initialize matrices using random orthogonal initialization [Saxe et al., 2014]. |