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..
Type-augmented Relation Prediction in Knowledge Graphs
Authors: Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula, Tian Gao, Qiang Ji7151-7159
AAAI 2021 | Venue PDF | 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. |