A knowledge inference model for question answering on an incomplete knowledge graph.

Question answering over knowledge graph (KGQA) is a task to solve natural language questions on knowledge graphs (KGs). Multi-hop KGQA requires multi-steps reasoning on the KG to find the correct answers to complex questions. However, it is difficult to find the triple required by the question directly when solving complex multi-hop questions for KGs with missing links. To mitigate this challenge, we propose an effective reasoning model that fuses neighbor interaction and a relation recognition module for multi-hop QA. Specifically, we adopt neighbor interaction networks to learn a better entity representation. The model identifies the relations contained in the questions through neural networks to further precisely determine the range of answers. Our method selectively captures the complex hidden information within the KG and overcomes the limitation of the answer range. It can perform well without the help of additional text corpora. The experimental results on two datasets show that our model can effectively capture richer semantic information for reasoning and achieve better results than all baseline models. [ABSTRACT FROM AUTHOR]