Semantic Parsing for Question Answering over Knowledge Graphs

In this paper, we propose a novel method for question answering over knowledge graphs based on graph-to-segment mapping, designed to improve the understanding of natural language questions. Our approach is grounded in semantic parsing, a key technique for interpreting question utterances. The main challenges arise from handling implicit entities and relations, as well as complex constraints such as temporal conditions, ordinality, and aggregation within the context of a knowledge graph. To address these issues, our framework integrates both rule-based and neural methods to parse and construct accurate, comprehensive semantic segment sequences. These sequences are then assembled into semantic query graphs, providing precise representations of question utterances. We formulate question semantic parsing as a sequence generation task, employing an encoder-decoder neural network to map natural language questions into semantic segments. Furthermore, to enhance the identification of implicit entities and relations, we incorporate a graph neural network that leverages knowledge graph context to enrich question representations. Experimental evaluations on two benchmark datasets demonstrate the effectiveness and superior performance of our model in semantic parsing for knowledge graph question answering.

Sijia Wei, Wenwen Zhang, Qisong Li, Jiang Zhao · 2023