Question understanding is one of the important tasks of question answering over knowledge graph

where semantic parsing is the mainstream approach for understanding question utterance. The most significant challenge in this task is to understand the implicit entities

relations and the utterances of complex constraints such as time

ordinal

and aggregation in the question with the context of knowledge graph. In this paper

we propose graph-to-segment

a semantic segments based semantic parsing framework for question answering over knowledge graph. Our semantic parsing model integrates both rule-based and neural-based approaches to parse the semantic segment sequences and constructs the semantic query graphs with high accuracy and coverage. These semantic segment-based semantic query graphs

which consist of the semantic segments

are used to represent the utterance of questions. Question semantic parsing is modeled as a sequence generation task

where an encoder-decoder neural network is used to generate the semantic segments from natural language questions. Additionally

with the context information of knowledge graph

a graph neural network is used to learn the representation of questions to improve the effect of semantic parsing on implicit entities or relations. Experimental results show that our model achieves good performance on the two datasets.