GraphQ IR: Unifying Semantic Parsing of Graph Query Language with Intermediate Representation

• URL: http://arxiv.org/abs/2205.12078v1
• Date: Tue, 24 May 2022 13:59:53 GMT
• Title: GraphQ IR: Unifying Semantic Parsing of Graph Query Language with Intermediate Representation
• Authors: Lunyiu Nie, Shulin Cao, Jiaxin Shi, Qi Tian, Lei Hou, Juanzi Li, Jidong Zhai
• Abstract summary: We propose a unified intermediate representation (IR) for graph query languages, namely GraphQ IR. With the IR's natural-language-like representation that bridges the semantic gap and its formally defined syntax that maintains the graph structure, neural semantic parsing can more effectively convert user queries into GraphQ IR. Our approach can consistently achieve state-of-the-art performance on KQA Pro, Overnight and MetaQA.
• Score: 91.27083732371453
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Subject to the semantic gap lying between natural and formal language, neural semantic parsing is typically bottlenecked by the paucity and imbalance of data. In this paper, we propose a unified intermediate representation (IR) for graph query languages, namely GraphQ IR. With the IR's natural-language-like representation that bridges the semantic gap and its formally defined syntax that maintains the graph structure, neural semantic parser can more effectively convert user queries into our GraphQ IR, which can be later automatically compiled into different downstream graph query languages. Extensive experiments show that our approach can consistently achieve state-of-the-art performance on benchmarks KQA Pro, Overnight and MetaQA. Evaluations under compositional generalization and few-shot learning settings also validate the promising generalization ability of GraphQ IR with at most 11% accuracy improvement.

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