PASSLEAF: A Pool-bAsed Semi-Supervised LEArning Framework for Uncertain Knowledge Graph Embedding
Keywords:Linked Open Data, Knowledge Graphs & KB Completio, Representation Learning, Semi-Supervised Learning, Graph-based Machine Learning
AbstractIn 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.
How to Cite
Chen, Z.-M., Yeh, M.-Y., & Kuo, T.-W. (2021). PASSLEAF: A Pool-bAsed Semi-Supervised LEArning Framework for Uncertain Knowledge Graph Embedding. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4019-4026. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16522
AAAI Technical Track on Data Mining and Knowledge Management