Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On Multi-Relational Link Prediction With Bilinear Models
Authors: Yanjie Wang, Rainer Gemulla, Hui Li
AAAI 2018 | Venue PDF | 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 EMAIL Rainer Gemulla University of Mannheim, Germany EMAIL Hui Li The University of Hong Kong EMAIL |
| 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. |