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..
Knowledge Hypergraphs: Prediction Beyond Binary Relations
Authors: Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also develop public datasets, benchmarks and baselines for hypergraph prediction and show experimentally that the proposed models are more effective than the baselines. |
| Researcher Affiliation | Collaboration | Bahare Fatemi1,2 , Perouz Taslakian2 , David Vazquez2 and David Poole1 1University of British Columbia 2Element AI |
| Pseudocode | No | Insufficient information. The paper describes the models and their functions but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data available at https://github.com/Element AI/Hyp E. |
| Open Datasets | Yes | The experiments on knowledge hypergraph completion are conducted on three datasets. The ο¬rst is JF17K proposed by Wen et al. (2016)... We also create two datasets FB-AUTO and M-FB15K from FREEBASE. For the experiments on datasets with binary relations, we use two standard benchmarks for knowledge graph completion: WN18 [Bordes et al., 2014] and FB15k [Bordes et al., 2013]. |
| Dataset Splits | Yes | Table 2: Dataset Statistics. |
| Hardware Specification | No | Insufficient information. The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | Insufficient information. The paper mentions 'Py Torch' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | Instead of tuning the parameters of Hyp E to get potentially better results, we follow the Kazemi and Poole (2018) setup with the same grid search approach by setting n = 2, l = 2, and s = 2. |