Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge
  Graph Completion
Tao He Ming Liu Yixin Cao Zekun Wang Zihao Zheng Zheng Chu Bing Qin
Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge
Graph Completion (KGC) methods, that is, the completion performance decreases
rapidly with the increase of graph sparsity. This problem is also exacerbated
because of the widespread existence of sparse KGs in practical applications. To
alleviate this challenge, we present a novel framework, LR-GCN, that is able to
automatically capture valuable long-range dependency among entities to
supplement insufficient structure features and distill logical reasoning
knowledge for sparse KGC. The proposed approach comprises two main components:
a GNN-based predictor and a reasoning path distiller. The reasoning path
distiller explores high-order graph structures such as reasoning paths and
encodes them as rich-semantic edges, explicitly compositing long-range
dependencies into the predictor. This step also plays an essential role in
densifying KGs, effectively alleviating the sparse issue. Furthermore, the path
distiller further distills logical reasoning knowledge from these mined
reasoning paths into the predictor. These two components are jointly optimized
using a well-designed variational EM algorithm. Extensive experiments and
analyses on four sparse benchmarks demonstrate the effectiveness of our
proposed method.