Neuro-Symbolic Query Compiler

2025-05-17

AI Summary

In resource-constrained settings, conventional RAG systems struggle to accurately identify the nested structural intent of complex queries. Method: This paper proposes a neuro-symbolic query compilation framework. Its core components are: (1) a minimal, complete BNF grammar ( G[q] ) that formally encodes the syntactic structure of complex queries; (2) a tripartite neuro-symbolic compilation pipeline—comprising query expression translation, lexical and syntactic parsing, and recursive-descent processing—that automatically compiles natural language queries into executable abstract syntax trees (ASTs); and (3) grammar-driven intent parsing coupled with semantics-preserving retrieval augmentation. Results: Experiments demonstrate substantial improvements in document retrieval accuracy and response generation quality for complex queries. The framework achieves high robustness, strong interpretability, and lightweight deployability under low-resource conditions.

Technology Category

Natural Language Processing: Question AnsweringData Mining & Knowledge Management: Intelligent Query ProcessingMachine Learning: Neuro-Symbolic Learning

Application Category

Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGGraph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with nested structures and dependencies. This paper presents QCompiler, a neuro-symbolic framework inspired by linguistic grammar rules and compiler design, to bridge this gap. It theoretically designs a minimal yet sufficient Backus-Naur Form (BNF) grammar $G[q]$ to formalize complex queries. Unlike previous methods, this grammar maintains completeness while minimizing redundancy. Based on this, QCompiler includes a Query Expression Translator, a Lexical Syntax Parser, and a Recursive Descent Processor to compile queries into Abstract Syntax Trees (ASTs) for execution. The atomicity of the sub-queries in the leaf nodes ensures more precise document retrieval and response generation, significantly improving the RAG system's ability to address complex queries.

Problem

• Precise recognition of search intent in RAG systems
• Handling complex queries with nested structures and dependencies
• Resource-efficient query compilation for improved document retrieval

Innovation

• Neuro-symbolic framework for query compilation
• Minimal Backus-Naur Form grammar for queries
• Recursive descent processor generates Abstract Syntax Trees