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 [1].
Knowledge Base Completion Using Embeddings and Rules
Authors: Quan Wang, Bin Wang, Li Guo
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two publicly available data sets show that our approach significantly and consistently outperforms state-of-the-art embedding models in KB completion. |
| Researcher Affiliation | Academia | Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China |
| Pseudocode | No | The paper presents the Integer Linear Programming (ILP) formulation in Figure 2, but it does not contain pseudocode or an algorithm block. |
| Open Source Code | No | We implement RESCAL and TRESCAL in Java, and use the code released by Bordes et al. [2013] for Trans E5. https://github.com/glorotxa/SME |
| Open Datasets | Yes | We create two data sets Location and Sport using NELL, both containing five relations (listed in Table 1) and the associated triples. |
| Dataset Splits | Yes | To evaluate, for each data set, we split the triples into a training set and a test set, with the ratio of 4:1. |
| Hardware Specification | No | The paper mentions computation times ('It takes about 1 minute on Location data and 2 hours on Sport data') but does not provide specific details about the hardware used for these computations (e.g., GPU/CPU models, memory). |
| Software Dependencies | Yes | We implement RESCAL and TRESCAL in Java, and use the code released by Bordes et al. [2013] for Trans E5. We use the lp solve package6 to solve the ILP problems. (footnote 6: http://lpsolve.sourceforge.net/5.5/) |
| Experiment Setup | Yes | In RESCAL and TRESCAL, we fix the regularization parameter λ to 0.1, and the maximal number of iterations to 10... In Trans E, we fix the margin to 1, the learning rate to 10, the batch number to 5, and the maximal number of iterations again to 10... For each of the three models, we tune the the latent dimension d in the range of {10, 20, 30, 40, 50} and select the optimal parameter setting. |