PASSLEAF: A Pool-bAsed Semi-Supervised LEArning Framework for Uncertain Knowledge Graph Embedding

Authors

  • Zhu-Mu Chen Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
  • Mi-Yen Yeh Institute of Information Science, Academia Sinica, Taipei, Taiwan
  • Tei-Wei Kuo Department of Computer Science, City University of Hong Kong, Hong Kong Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan Graduate Institute of Networking and Multimedia, National Taiwan University, Taiwan

Keywords:

Linked Open Data, Knowledge Graphs & KB Completio, Representation Learning, Semi-Supervised Learning, Graph-based Machine Learning

Abstract

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.

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Published

2021-05-18

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

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management