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

• URL: http://arxiv.org/abs/2505.22299v2
• Date: Thu, 29 May 2025 04:07:47 GMT
• Title: Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries
• Authors: Ganlin Xu, Zhoujia Zhang, Wangyi Mei, Jiaqing Liang, Weijia Lu, Xiaodong Zhang, Zhifei Yang, Xiaofeng Ma, Yanghua Xiao, Deqing Yang,
• Abstract summary: Current dense retrievers retrieve the relevant documents within a corpus via embedding similarities.<n>We propose a neuro-symbolic information retrieval method, namely textbfNS-IR, to optimize the embeddings of naive natural language.<n>Our experiments demonstrate that NS-IR achieves superior zero-shot retrieval performance on web search and low-resource retrieval tasks.
• Score: 36.93438185371322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \textbf{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 \textbf{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.

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