Embedding Uncertain Knowledge Graphs

Authors: Xuelu Chen, Muhao Chen, Weijia Shi, Yizhou Sun, Carlo Zaniolo3363-3370

AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification.
Researcher Affiliation Academia University of California, Los Angeles Los Angeles, CA, 90095, USA {shirleychen, muhaochen, swj0419, yzsun, zaniolo}@cs.ucla.edu
Pseudocode No The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present structured code blocks.
Open Source Code No The paper does not contain any explicit statements about making the source code available, nor does it provide a link to a code repository.
Open Datasets Yes The evaluation is conducted on three datasets named as CN15k, NL27k, and PPI5k, which are extracted from Concept Net, NELL, and the Protein-Protein Interaction Knowledge Base STRING (Szklarczyk et al. 2016) respectively.
Dataset Splits Yes We split each dataset into three parts: 85% for training, 7% for validation, and 8% for testing.
Hardware Specification No The paper does not specify any particular hardware components (e.g., CPU, GPU models) used for running the experiments.
Software Dependencies No The paper mentions using 'Adam optimizer' and 'the implementation given by (Trouillon et al. 2016)' but does not provide specific version numbers for any software dependencies.
Experiment Setup Yes We select among the following sets of hyperparameter values: learning rate lr {0.001, 0.005, 0.01}, dimensionality k {64, 128, 256, 512}, batch size b {128, 256, 512, 1024}, The L2 regularization coefficient λ is fixed as 0.005. Training was stopped using early stopping based on MSE on the validation set, computed every 10 epochs.