mlsanityData to 14 Oct 2026

GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation

arXiv 2205.12078 ↗First posted 24 May 2022cs.CL cs.DBcited by 13

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Abstract (arXiv)

Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax. Recent works have proposed several forms of supplementary supervision but none is generalized across multiple formal languages. This paper proposes a unified intermediate representation (IR) for graph query languages, named GraphQ IR. It has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. Therefore, a neural semantic parser can more precisely convert user queries into GraphQ IR, which can be later losslessly compiled into various downstream graph query languages. Extensive experiments on several benchmarks including KQA Pro, Overnight, GrailQA, and MetaQA-Cypher under standard i.i.d., out-of-distribution, and low-resource settings validate GraphQ IR's superiority over the previous state-of-the-arts with a maximum 11% accuracy improvement.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 49 of its 93 references that are in the map. First posted on arXiv 24 May 2022.

Builds on

49 of 93 references.

References · 49

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37 more
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Citing papers per year: 2023: 5; 2024: 5; 2025: 1; 2026: 2.

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  1. 2023Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Familycited by 71
  2. 2023Text-to-SQL Error Correction with Language Models of Codecited by 21
  3. 2024CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Eracited by 16
  4. 2023Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoningcited by 7
  5. 2024Multi-hop Question Answering over Knowledge Graphs using Large Language Modelscited by 6
  6. 2024How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answeringcited by 5
  7. 2023VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answeringcited by 2
  8. 2023An In-Context Schema Understanding Method for Knowledge Base Question Answeringcited by 1
  9. 2024KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Basescited by 1
  10. 2024Semantic Parsing with Candidate Expressions for Knowledge Base Question Answeringcited by 1
  11. 2025ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphscited by 1
  12. 2026Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generationcited by 1
1 more
  1. 2026Graph Query Generation with Constraint-guided Large Language Agentscited by 0