Graph Matching Network for Interpretable Complex Question Answering over Knowledge Graphs

Question answering over knowledge graphs is a trending research topic in artificial intelligence. In this task, the semantic matching between the structures of a natural language question and a knowledge graph is a challenging research problem. Existing works mainly use a sequence-based deep neural encoder to process questions. They construct a semantic matching model to compute the similarity between question structures and subgraphs of a knowledge graph. However, they could not exploit the structure of a complex question, and they lack interpretability. To alleviate this issue, this paper presents a graph matching network (GMN) based method for answering complex questions of a knowledge graph, called TTQA. This method firstly constructs an ungrounded query graph which is independent of the knowledge graph via syntactic parsing. Then, based on the ungrounded query graph and the knowledge graph, this method constructs a grounded query graph which is dependent on the knowledge graph. In particular, this paper proposes a cross-graph attention GMN which combines pre-trained language model and graph neural network to learn the context representation of a query. The context representation enhances the representation of graph matching which helps to predict a grounded query. Experimental results show that TTQA achieves state-of-the-art results on LC-QuAD 1.0 and ComplexWebQuestions 1.1. Ablation studies demonstrate the effectiveness of GMN. In addition, TTQA keeps the ungrounded query and the grounded query to enhance the interpretability of question answering.