Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries

2025-05-28

AI Summary

To address the low relevance of dense retrievers on complex queries involving negative constraints—caused by their neglect of semantic intent—this paper proposes Neural-Symbolic Integrated Retrieval (NS-IR). NS-IR is the first dense retrieval framework to incorporate First-Order Logic (FOL) modeling, enabling fine-grained re-ranking via logic consistency discrimination, logic alignment, and connective-aware constraint enforcement, while jointly optimizing logic-driven embedding representations. We introduce NegConstraint, the first benchmark dataset specifically designed for queries with negative constraints. Experiments demonstrate that NS-IR significantly outperforms state-of-the-art methods on negative-constraint queries; it also achieves superior performance in zero-shot web search and low-resource settings. The code and dataset are publicly released.

Technology Category

• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Information retrieval plays a crucial role in resource localization. Current dense retrievers retrieve the relevant documents within a corpus via embedding similarities, which compute similarities between dense vectors mainly depending on word co-occurrence between queries and documents, but overlook the real query intents. Thus, they often retrieve numerous irrelevant documents. Particularly in the scenarios of complex queries such as emph{negative-constraint queries}, their retrieval performance could be catastrophic. To address the issue, we propose a neuro-symbolic information retrieval method, namely extbf{NS-IR}, that leverages first-order logic (FOL) to optimize the embeddings of naive natural language by considering the emph{logical consistency} between queries and documents. Specifically, we introduce two novel techniques, emph{logic alignment} and emph{connective constraint}, to rerank candidate documents, thereby enhancing retrieval relevance. Furthermore, we construct a new dataset extbf{NegConstraint} including negative-constraint queries to evaluate our NS-IR's performance on such complex IR scenarios. Our extensive experiments demonstrate that NS-IR not only achieves superior zero-shot retrieval performance on web search and low-resource retrieval tasks, but also performs better on negative-constraint queries. Our scource code and dataset are available at https://github.com/xgl-git/NS-IR-main.

Problem

• Improves retrieval relevance for negative-constraint queries
• Addresses logical consistency between queries and documents
• Enhances zero-shot performance in complex IR scenarios

Innovation

• Neuro-symbolic method using first-order logic
• Logic alignment and connective constraint techniques
• New dataset for negative-constraint queries