Learning to Denoise Unreliable Interactions for Link Prediction on
  Biomedical Knowledge Graph
Tengfei Ma Yujie Chen Wen Tao Dashun Zheng Xuan Lin Patrick Cheong-lao Pang Yiping Liu Yijun Wang Bosheng Song Xiangxiang Zeng
Link prediction in biomedical knowledge graphs (KGs) aims at predicting
unknown interactions between entities, including drug-target interaction (DTI)
and drug-drug interaction (DDI), which is critical for drug discovery and
therapeutics. Previous methods prefer to utilize the rich semantic relations
and topological structure of the KG to predict missing links, yielding
promising outcomes. However, all these works only focus on improving the
predictive performance without considering the inevitable noise and unreliable
interactions existing in the KGs, which limits the development of KG-based
computational methods. To address these limitations, we propose a Denoised Link
Prediction framework, called DenoisedLP. DenoisedLP obtains reliable
interactions based on the local subgraph by denoising noisy links in a
learnable way, providing a universal module for mining underlying task-relevant
relations. To collaborate with the smoothed semantic information, DenoisedLP
introduces the semantic subgraph by blurring conflict relations around the
predicted link. By maximizing the mutual information between the reliable
structure and smoothed semantic relations, DenoisedLP emphasizes the
informative interactions for predicting relation-specific links. Experimental
results on real-world datasets demonstrate that DenoisedLP outperforms
state-of-the-art methods on DTI and DDI prediction tasks, and verify the
effectiveness and robustness of denoising unreliable interactions on the
contaminated KGs.