Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
Authors: Tengwei Song, Jie Luo, Lei Huang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed Rot-Pro model effectively learns the transitivity pattern and achieves the state-of-the-art results on the link prediction task in the datasets containing transitive relations. |
| Researcher Affiliation | Academia | Tengwei Song, Jie Luo , Lei Huang State Key Laboratory of Software Development Environment Beihang University, Beijing, 100191 {songtengwei,luojie,huangleiai}@buaa.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We evaluate the Rot-Pro model on four well-known benchmarks. In general, FB15k-237 and WN18RR are two widely-used benchmarks and YAGO3-10 and Countries are two benchmarks with abundant relation patterns including transitivity. ... FB15k-237 [24] ... WN18RR [23] ... YAGO3-10 [13] ... Countries [8] |
| Dataset Splits | No | The paper states that "corrupt triples that appear in training, validation, or test sets are removed during ranking." indicating the use of a validation set. However, it does not provide specific details on the validation split (e.g., percentages or sample counts), nor does it reference predefined splits with such information. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | With the hyper-parameters introduced, we train Rot-Pro using a grid search of hyper-parameters: fixed margin γ in Equation 9 {0.1, 4.0, 6.0, 9.0, 16.0, 20.0}, weights tuning hyper-parameters for loss, α {0.0001, 0.0005, 0.0008}, value of γm in Equation 10 {1e 6, 5e 6, 1e 5}, value of β in Equation 10 {1.3, 1.5, 2.0}. |