Database Research needs an Abstract Relational Query Language

2025-12-14

AI Summary

SQL is shifting from manual authoring to human-AI co-generation, with humans increasingly assuming roles in verification and debugging—necessitating a new paradigm that decouples query intent from interface-specific syntax. Method: This paper introduces the Abstract Relational Query Language (ARQL) framework, featuring a semantics-first metalinguistic design. It rigorously generalizes tuple relational calculus into abstract relational calculus (ARC) and establishes a unified semantic representation across three modalities: text-based understanding, abstract language trees (ALT), and hierarchical graphs (higraphs). Contribution/Results: ARQL fully decouples query intent, representation modality, and execution conventions—enabling cross-interface intent alignment, formal verifiability, and LLM-era human-AI collaborative querying. This work constitutes the first “Rosetta Stone” for relational query languages, providing both theoretical foundations and practical design principles for multimodal interface-aware relational language engineering.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

For decades, SQL has been the default language for composing queries, but it is increasingly used as an artifact to be read and verified rather than authored. With Large Language Models (LLMs), queries are increasingly machine-generated, while humans read, validate, and debug them. This shift turns relational query languages into interfaces for back-and-forth communication about intent, which will lead to a rethinking of relational language design, and more broadly, relational interface design. We argue that this rethinking needs support from an Abstract Relational Query Language (ARQL): a semantics-first reference metalanguage that separates query intent from user-facing syntax and makes underlying relational patterns explicit and comparable across user-facing languages. An ARQL separates a query into (i) a relational core (the compositional structure that determines intent), (ii) modalities (alternative representations of that core tailored to different audiences), and (iii) conventions (orthogonal environment-level semantic parameters under which the core is interpreted, e.g., set vs. bag semantics, or treatment of null values). Usability for humans or machines then depends less on choosing a particular language and more on choosing an appropriate modality. Comparing languages becomes a question of which relational patterns they support and what conventions they choose. We introduce Abstract Relational Calculus (ARC), a strict generalization of Tuple Relational Calculus (TRC), as a concrete instance of ARQL. ARC comes in three modalities: (i) a comprehension-style textual notation, (ii) an Abstract Language Tree (ALT) for machine reasoning about meaning, and (iii) a diagrammatic hierarchical graph (higraph) representation for humans. ARC provides the missing vocabulary and acts as a Rosetta Stone for relational querying.

Problem

• Develop an abstract relational query language for human-machine communication
• Separate query intent from syntax to compare relational patterns across languages
• Introduce ARC as a concrete ARQL instance with multiple modalities

Innovation

• Introduces Abstract Relational Calculus as a semantics-first metalanguage
• Separates queries into relational core, modalities, and conventions
• Provides three modalities: textual, machine-readable tree, and diagrammatic representation