Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems

2025-03-01

AI Summary

AI-augmented Data Processing Systems (DPS) suffer from low trustworthiness in critical applications due to the inherent unreliability of large language model (LLM) outputs, while existing constraint mechanisms are fragmented, imperative, and lack semantic-aware query execution support. Method: We propose Semantic Integrity Constraints (SICs)—a declarative abstraction embedded within relational models—extending classical database integrity constraints to the semantic layer for the first time. We introduce novel constraint classes (e.g., groundedness), unify active constraint decoding with passive verification-and-recovery execution, and extend relational algebra to enable query-aware optimization and execution. Contribution/Results: Our framework significantly improves DPS trustworthiness and performance, supports enterprise-scale deployment, and establishes foundational theory and systems infrastructure for constraint-driven semantic query optimization and adaptive execution.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The emergence of AI-augmented Data Processing Systems (DPSs) has introduced powerful semantic operators that extend traditional data management capabilities with LLM-based processing. However, these systems face fundamental reliability (a.k.a. trust) challenges, as LLMs can generate erroneous outputs, limiting their adoption in critical domains. Existing approaches to LLM constraints--ranging from user-defined functions to constrained decoding--are fragmented, imperative, and lack semantics-aware integration into query execution. To address this gap, we introduce Semantic Integrity Constraints (SICs), a novel declarative abstraction that extends traditional database integrity constraints to govern and optimize semantic operators within DPSs. SICs integrate seamlessly into the relational model, allowing users to specify common classes of constraints (e.g., grounding and soundness) while enabling query-aware enforcement and optimization strategies. In this paper, we present the core design of SICs, describe their formal integration into query execution, and detail our conception of grounding constraints, a key SIC class that ensures factual consistency of generated outputs. In addition, we explore novel enforcement mechanisms, combining proactive (constrained decoding) and reactive (validation and recovery) techniques to optimize efficiency and reliability. Our work establishes SICs as a foundational framework for trustworthy, high-performance AI-augmented data processing, paving the way for future research in constraint-driven optimizations, adaptive enforcement, and enterprise-scale deployments.

Problem

• Address reliability challenges in AI-augmented data processing systems.
• Introduce Semantic Integrity Constraints for semantic operator governance.
• Ensure factual consistency and optimize efficiency in query execution.

Innovation

• Declarative Semantic Integrity Constraints (SICs)
• Integration of SICs into relational models
• Proactive and reactive enforcement mechanisms