On Multi-Relational Link Prediction With Bilinear Models
Authors: Yanjie Wang, Rainer Gemulla, Hui Li
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report results of an independent experimental study that evaluates recent bilinear models in a common experimental setup. |
| Researcher Affiliation | Academia | Yanjie Wang University of Mannheim, Germany ywang@uni-mannheim.de Rainer Gemulla University of Mannheim, Germany rgemulla@uni-mannheim.de Hui Li The University of Hong Kong hli2@cs.hku.hk |
| Pseudocode | No | The paper does not contain any explicit pseudocode blocks or algorithms. |
| Open Source Code | Yes | All datasets, experimental results, and source code will be made publicly available.4 http://dws.informatik.uni-mannheim.de/en/resources/software/tf/ |
| Open Datasets | Yes | We used the WN18 (Bordes et al. 2014) and FB15K (Bordes et al. 2013) datasets, which were extracted from Word Net (Miller 1995) and Freebase (Bollacker et al. 2008), respectively. |
| Dataset Splits | Yes | The two datasets are presplit into a training set, a validation set, and a test set. Table 2 summarizes the key statistics. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions C++, Intel Math Kernel Library, Adagrad, and LIBLINEAR, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We considered the following hyperparameter settings: r {100, 200}, learning rate η {0.01, 0.1, 1}, weight of L2-regularization λe, λr {0, 0.1, 0.01} for entity and relation parameters, resp., margin hyperparameter γ {1, 2, 4, 8} for RESCAL, γ {0.2, 0.5, 0.7} for Hol E, and γ {0.2, 0.5, 0.7, 1.0, 1.5} for Trans E. ... Tab. 3 reports the hyperparameters ultimately selected. |