Type-augmented Relation Prediction in Knowledge Graphs
Authors: Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula, Tian Gao, Qiang Ji7151-7159
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed Ta RP method achieves significantly better performance than state-of-the-art methods on four benchmark datasets: FB15K, FB15K-237, YAGO26K-906, and DB111K-174. |
| Researcher Affiliation | Collaboration | Zijun Cui,1 Pavan Kapanipathi,2 Kartik Talamadupula,2 Tian Gao,2 Qiang Ji1 1Rensselaer Polytechnic Institute 2 IBM Research |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We consider three benchmark datasets for the relation prediction task: FB15K (Bordes et al. 2013), YAGO26K-906 (Hao et al. 2019) and DB111K-74 (Hao et al. 2019). |
| Dataset Splits | Yes | On each dataset, we select the threshold η from {0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9} that achieves the best relation prediction performance (Hits@1) on the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | Ta RP has one hyper-parameter threshold η. On each dataset, we select the threshold η from {0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9} that achieves the best relation prediction performance (Hits@1) on the validation set. On FB15K and YAGO26K-906, η = 0.1. On DB111K-174, η = 0. |