Semantic Segment Based Semantic Parsing for Question Answering over Knowledge Graphs

In this paper, we introduce a novel method named "graph-to-segment" for question answering over knowledge graphs, focusing on understanding question utterances. This method centers on semantic parsing, a key approach for interpreting these utterances. Our primary challenge lies 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.