Noisy Positive-Unlabeled Learning with Self-Training for Speculative
  Knowledge Graph Reasoning
Ruijie Wang Baoyu Li Yichen Lu Dachun Sun Jinning Li Yuchen Yan Shengzhong Liu Hanghang Tong Tarek F. Abdelzaher
This paper studies speculative reasoning task on real-world knowledge graphs
(KG) that contain both \textit{false negative issue} (i.e., potential true
facts being excluded) and \textit{false positive issue} (i.e., unreliable or
outdated facts being included). State-of-the-art methods fall short in the
speculative reasoning ability, as they assume the correctness of a fact is
solely determined by its presence in KG, making them vulnerable to false
negative/positive issues. The new reasoning task is formulated as a noisy
Positive-Unlabeled learning problem. We propose a variational framework, namely
nPUGraph, that jointly estimates the correctness of both collected and
uncollected facts (which we call \textit{label posterior}) and updates model
parameters during training. The label posterior estimation facilitates
speculative reasoning from two perspectives. First, it improves the robustness
of a label posterior-aware graph encoder against false positive links. Second,
it identifies missing facts to provide high-quality grounds of reasoning. They
are unified in a simple yet effective self-training procedure. Empirically,
extensive experiments on three benchmark KG and one Twitter dataset with
various degrees of false negative/positive cases demonstrate the effectiveness
of nPUGraph.