Non-translational Alignment for Multi-relational Networks
Authors: Shengnan Li, Xin Li, Rui Ye, Mingzhong Wang, Haiping Su, Yingzi Ou
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experiments on four multi-lingual knowledge graphs demonstrate the effectiveness and robustness of the proposed method over a set of stateof-the-art alignment methods. |
| Researcher Affiliation | Academia | 1 School of Computer Science, Beijing Institute of Technology, China 2 School of Business, University of the Sunshine Coast, Australia |
| Pseudocode | No | The paper describes the model and inference process mathematically and textually, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing its own source code or a link to a repository for the described methodology. |
| Open Datasets | Yes | trilingual datasets WK31-15k and WK31-120k [Chen et al., 2017], including English(En), German(De) and French(Fr) knowledge graphs which are extracted from DBpedia with known aligned entities as ground truth. |
| Dataset Splits | No | The paper mentions 'training ratios' and 'test-to-training ratios' but does not explicitly specify a validation set or its split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The embedding dimension is set as 100. [...] To infer the vector representations of networks, stochastic gradient descent is applied for optimization. [...] Negative sampling [Mikolov et al., 2013] is applied... [...] with training ratios as 80% for entity anchors, 100% for relation anchors |