HGMAN: Multi-Hop and Multi-Answer Question Answering Based on Heterogeneous Knowledge Graph (Student Abstract)

  • Xu Wang Beijing University of Posts and Telecommunications
  • Shuai Zhao Beijing University of Posts and Telecommunications
  • Bo Cheng Beijing University of Posts and Telecommunications
  • Jiale Han Beijing University of Posts and Telecommunications
  • Yingting Li Beijing University of Posts and Telecommunications
  • Hao Yang Huawei Technologies Co., Ltd
  • Guoshun Nan Singapore University of Technology and Design

Abstract

Multi-hop question answering models based on knowledge graph have been extensively studied. Most existing models predict a single answer with the highest probability by ranking candidate answers. However, they are stuck in predicting all the right answers caused by the ranking method. In this paper, we propose a novel model that converts the ranking of candidate answers into individual predictions for each candidate, named heterogeneous knowledge graph based multi-hop and multi-answer model (HGMAN). HGMAN is capable of capturing more informative representations for relations assisted by our heterogeneous graph, which consists of multiple entity nodes and relation nodes. We rely on graph convolutional network for multi-hop reasoning and then binary classification for each node to get multiple answers. Experimental results on MetaQA dataset show the performance of our proposed model over all baselines.

Published
2020-04-03
Section
Student Abstract Track