Publication - Paper

The adaptive margin in hTransM

Date

    2016/10 - 2017/03

Authors

    Manling Li, Denghui Zhang, Yantao Jia, Yuanzhuo Wang, Xueqi Cheng

Paper

Problem

    50% triples in knowledge graphs are organized in hierarchical structures, which also contain rich inference patterns to predict links but do not be fully used. A main feature of hierarchical structure is multi-layer, which traditional methods fails to make use of.

My Work

  • Came up with the idea to divide the hierarchical structures into two categories, i.e., single-step hierarchical structures and multi-step hierarchical structures.
  • Proposed multi-step specific margin, and the link prediction method based on detecting single-step and multi-step hierarchical structures, called hTransM. It can adaptively determine the optimal margin to separate positive and negative triples.
  • Proved the convergence of hTransM by demonstrating its uniform stability and provide the upper bound of the error of the proposed model.
  • Experimented hTransM over 3 datasets of 2 sub-tasks, i.e., entity prediction task and relation prediction task, which demonstated that hTransM can achieve better prediction performance.
  • Studyed the variation of the optimal margin value along with the optimization process, and compared with the methods withnot considering hierarchical information. It validated the superiority of hierarchy-constrained margin.
  • The paper was submitted to IEEE Transaction on Big Data (Lead Author) and under review now.