  In this paper, we introduce a novel method with graph-to-segment mapping for
question answering over knowledge graphs, which helps understanding question
utterances. This method centers on semantic parsing, a key approach for
interpreting these utterances. The challenges lie in comprehending implicit
entities, relationships, and complex constraints like time, ordinality, and
aggregation within questions, contextualized by the knowledge graph. Our
framework employs a combination of rule-based and neural-based techniques to
parse and construct highly accurate and comprehensive semantic segment
sequences. These sequences form semantic query graphs, effectively representing
question utterances. We approach question semantic parsing as a sequence
generation task, utilizing an encoder-decoder neural network to transform
natural language questions into semantic segments. Moreover, to enhance the
parsing of implicit entities and relations, we incorporate a graph neural
network that leverages the context of the knowledge graph to better understand
question representations. Our experimental evaluations on two datasets
demonstrate the effectiveness and superior performance of our model in semantic
parsing for question answering.
