TQL: Towards Type-Driven Data Discovery

2025-08-11

AI Summary

Existing data discovery query languages are predominantly system-driven, neglecting user intent and downstream task context. Method: We propose Type-aware Query Language (TQL), the first formally unified framework for data discovery that integrates type-theoretic principles—providing rigorous, simultaneous formalization of syntax, semantics, and evaluation models. TQL explicitly encodes user intent and transformation context via type constraints, enabling context-aware query construction and result validation. Grounded in programming language theory, we design a query framework with strong derivability and extensibility, and implement a prototype system. Contribution/Results: Experiments demonstrate that TQL significantly outperforms state-of-the-art approaches in expressive power, flexibility, and adaptability to real-world scenarios, establishing a foundation for intent- and context-aware data discovery.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Natural Language Processing: Question Answering
• Knowledge Representation and Reasoning: Description Logics

Application Category

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

Abstract

Existing query languages for data discovery exhibit system-driven designs that emphasize database features and functionality over user needs. We propose a re-prioritization of the client through an introduction of a language-driven approach to data discovery systems that can leverage powerful results from programming languages research. In this paper, we describe TQL, a flexible and practical query language which incorporates a type-like system to encompass downstream transformation-context in its discovery queries. The syntax and semantics of TQL (including the underlying evaluation model), are formally defined, and a sketch of its implementation is also provided. Additionally, we provide comparisons to existing languages for data retrieval and data discovery to examine the advantages of TQL's expanded expressive power in real-life settings.

Problem

• Existing query languages prioritize system features over user needs
• TQL introduces a type-driven approach for data discovery
• TQL enhances expressive power for real-life data retrieval contexts

Innovation

• Type-driven query language for data discovery
• Incorporates type-like system for transformation-context
• Formally defined syntax and evaluation model