@article{Chen_Yeh_Kuo_2021, title={PASSLEAF: A Pool-bAsed Semi-Supervised LEArning Framework for Uncertain Knowledge Graph Embedding}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/16522}, DOI={10.1609/aaai.v35i5.16522}, abstractNote={In this paper, we study the problem of embedding uncertain knowledge graphs, where each relation between entities is associated with a confidence score. Observing the existing embedding methods may discard the uncertainty information, only incorporate a specific type of score function, or cause many false-negative samples in the training, we propose the PASSLEAF framework to solve the above issues. PASSLEAF consists of two parts, one is a model that can incorporate different types of scoring functions to predict the relation confidence scores and the other is the semi-supervised learning model by exploiting both positive and negative samples associated with the estimated confidence scores. Furthermore, PASSLEAF leverages a sample pool as a relay of generated samples to further augment the semi-supervised learning. Experiment results show that our proposed framework can learn better embedding in terms of having higher accuracy in both the confidence score prediction and tail entity prediction.}, number={5}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Zhu-Mu and Yeh, Mi-Yen and Kuo, Tei-Wei}, year={2021}, month={May}, pages={4019-4026} }