A dynamic graph expansion network for multi-hop knowledge base question answering

Abstract

Knowledge base question answering aims to answer a question over a knowledge base. Multi-hop knowledge base question answering is a challenging task because it requires multi-step reasoning according to the question to get the answer. Existing models infer the answer over a static subgraph or attend to different parts of the question through an intermediate signal. The former obtains limited semantic information, and the latter provides limited reasoning due to the weak supervision signal. In this paper, we eliminate the limitation of static subgraph reasoning by dynamically expanding subgraphs, which connect the question and subgraph to form a joint subgraph. We then adjust the dynamic subgraph to enable reasoning at each step. Specifically, at each step, the question connects different subgraphs, respects the context while paying attention to a specific part of the question, generates a strong intermediate signal, acts on the subsequent reasoning, and finally obtains a correct answer. A large number of experiments on three datasets show that our method performs better than previous state-of-the-art models.