Logic-Guided Data Extraction with Answer Set Programming and Large Language Models
Proposes a logic-guided data extraction framework combining ASP and LLMs, reducing calls by 40% while maintaining accuracy.
Key Findings
Methodology
The approach integrates candidate facts generated by LLMs with ASP-based reasoning, leveraging ASP's non-monotonic logic to validate, infer, and ensure consistency during extraction. The core mechanism involves interleaving LLM calls with ASP inference, where guard conditions—expressed as conjunctive queries—determine whether to invoke the oracle for specific predicates. A guard caching mechanism exploits monotonicity to optimize performance. The framework formalizes the extraction process, proving equivalence to baseline methods under guard-respecting oracle assumptions, while significantly reducing LLM calls. Experiments on ASP benchmark datasets demonstrate approximately 40% reduction in oracle invocations and a 15% accuracy improvement, validating the effectiveness of active logical guidance.
Key Results
- On standard ASP benchmarks, the framework reduced oracle calls from an average of 10 to 6 per predicate, with overall extraction accuracy reaching 92%, outperforming traditional independent extraction approaches. Early inconsistency detection minimized false facts, improving data reliability.
- In complex relational and hierarchical scenarios, the method maintained stable performance and efficiency, especially in dense dependency networks. Ablation studies confirmed guard caching's role in reducing 30% of calls on large datasets.
- The results confirm that logical control not only cuts down resource usage but also enhances extraction quality by filtering out spurious outputs, demonstrating a practical advantage over naive methods.
Significance
This work advances semantic data extraction by embedding ASP's non-monotonic reasoning directly into the extraction pipeline, addressing key challenges of efficiency and correctness. It enables active, logic-driven control over large language models, reducing unnecessary calls and improving data quality. Such integration is crucial for scalable knowledge graph construction, automated reasoning, and AI explainability, bridging the gap between neural models and symbolic reasoning. The framework's formal guarantees and empirical validation suggest broad applicability in complex, real-world NLP tasks, marking a significant step toward neuro-symbolic AI systems.
Technical Contribution
The paper introduces a formal, modular framework that interleaves ASP reasoning with LLM-based extraction, defining guard conditions and logical inference steps. It proves the equivalence of the logic-guided process to baseline extraction under guard-respecting oracle assumptions, while offering optimization via guard caching based on monotonicity. The implementation details include an algorithm that dynamically evaluates guards, invokes the oracle only when necessary, and reuses guard outcomes, significantly reducing LLM calls without sacrificing completeness. This approach opens new avenues for scalable, explainable neuro-symbolic information extraction.
Novelty
This is the first systematic integration of ASP's non-monotonic reasoning into the active control of LLM-based extraction, moving beyond post-hoc validation to real-time, logic-driven decision-making. Unlike prior works that treat logic as a static validation step, this framework dynamically guides extraction, leveraging ASP's expressive power to prune unnecessary oracle calls and infer implicit facts. The combination of guard-based admissibility, incremental reasoning, and cache optimization represents a novel contribution to neuro-symbolic AI, setting a new standard for efficient, reliable semantic extraction.
Limitations
- The framework assumes the oracle respects guard conditions; if the oracle produces facts inconsistent with guards, the guarantees break down. Handling noisy or biased oracle outputs remains challenging.
- ASP reasoning incurs computational overhead, especially in highly complex or densely connected domains, potentially limiting real-time applicability.
- Designing effective guard conditions requires domain expertise and manual tuning, which may hinder scalability and automation. Future work should focus on adaptive guard learning and scalability improvements.
Future Work
Future directions include integrating adaptive guard learning mechanisms, extending the framework to multi-modal data, and exploring hybrid neural-symbolic models that combine deep learning with ASP-based control. Additionally, optimizing ASP solvers for large-scale, real-time applications and automating guard generation will be key to broader adoption. Investigating robustness against noisy oracle outputs and expanding the framework to handle more complex reasoning tasks are also promising avenues.
AI Executive Summary
Semantic data extraction from unstructured text has become a cornerstone of AI applications like knowledge graph construction and automated reasoning. However, existing approaches relying solely on large language models (LLMs) often face challenges related to efficiency, accuracy, and logical consistency. These models tend to generate candidate facts independently, leading to redundant calls, increased computational costs, and the risk of spurious information. To address these issues, this paper introduces a novel framework that actively guides the extraction process using Answer Set Programming (ASP), a form of non-monotonic logic programming.
The core idea is to interleave ASP reasoning with LLM calls, where logic-based guard conditions determine whether an oracle invocation is necessary at each step. This dynamic control mechanism allows the system to leverage existing facts and logical inferences to avoid unnecessary queries, early detect inconsistencies, and infer implicit facts without additional extraction. The framework formalizes this process, proving that under a guard-respecting oracle assumption, it is equivalent to traditional extraction pipelines but with significantly fewer LLM calls—about 40% less—while maintaining or improving accuracy.
Experimental validation on ASP benchmark datasets demonstrates that the proposed approach not only reduces resource consumption but also enhances data quality by filtering out false positives. The introduction of a guard caching mechanism further optimizes performance by exploiting the monotonicity of conjunctive queries, reducing redundant reasoning steps. This work represents a significant step toward neuro-symbolic AI, combining the generative power of LLMs with the structured reasoning capabilities of ASP. It opens new pathways for scalable, explainable, and reliable semantic data extraction, with broad implications for knowledge management, AI reasoning, and automated information systems. Future research will focus on automating guard generation, extending to multi-modal data, and improving scalability for real-time applications.
Deep Analysis
Background
随着深度学习和预训练模型的快速发展，语义信息抽取已成为自然语言处理的核心任务之一。早期方法依赖规则和模板，效果有限，难以适应复杂场景。近年来，大模型（如GPT、BERT）推动了端到端的抽取技术，显著提升了性能，但缺乏推理能力，容易生成伪信息。神经符号方法（如DeepProbLog、NeurASP）试图结合符号推理与神经网络，增强推理能力，但多为后验验证或迭代优化，未能在提取流程中主动引导推理。现有工作多忽视提取过程中的逻辑引导，导致调用频繁且效率低下。本文在此基础上，提出在提取过程中引入ASP推理，主动控制信息采集，解决效率与准确性难题。
Core Problem
传统的语义抽取方法多采用独立调用大模型生成候选事实，缺乏逻辑推理引导，导致大量冗余调用和伪信息。复杂关系和层次结构中，推理依赖全局一致性，单纯依赖生成模型难以保证。现有方法未能充分利用逻辑依赖关系，限制了抽取效率和质量。如何在保证抽取完整性的同时，减少调用次数、提升推理能力，成为核心难题。特别是在多关系、多层次结构的场景中，传统方法难以兼顾效率与准确性。
Innovation
本研究的创新点包括：1）引入ASP的非单调推理能力，主动引导信息采集，避免不必要的调用；2）定义守卫条件，依据逻辑推理动态控制LLM调用，减少冗余；3）结合ASP推理，提前检测不一致和隐含事实，提升数据可靠性；4）引入守卫缓存机制，利用守卫的单调性优化性能。这些创新使提取流程更智能、更高效，突破了传统神经符号分离的限制，为神经符号一体化提供新路径。
Methodology
- �� 构建逻辑导向的提取配置，包括目标谓词、守卫和逻辑程序；• 在每次提取前，依据ASP推理判断守卫条件是否满足；• 满足条件时，调用LLM生成候选事实，并加入数据库；• 结合ASP推理，提前检测不一致和隐含事实，避免后续冗余调用；• 利用守卫的单调性，缓存守卫结果，优化性能。整个流程在保证抽取完整性基础上，动态调整提取策略，确保每次调用都具有逻辑依据。
Experiments
采用ASP基准集（如：图着色、调度等）进行评估。对比传统独立调用方法，测量调用次数、抽取准确率和推理速度。设置不同复杂度场景，验证框架在多关系、多层次结构中的表现。通过消融实验，分析守卫缓存和推理策略的贡献。指标包括：调用次数减少比例、抽取精度、系统响应时间等。实验结果验证了逻辑引导的有效性和优化效果。
Results
在ASP基准集上，调用次数平均减少40%，抽取准确率提升至92%，优于传统方法。复杂结构场景中，系统表现出更高的稳定性和效率，特别在关系密集的场景中效果明显。守卫缓存机制在大规模数据集上减少了30%的调用，验证了守卫单调性在优化中的作用。整体结果显示，逻辑引导策略在保证抽取完整性的同时，有效提升了效率和数据质量。
Applications
该框架适用于知识图谱构建、自动问答、智能信息管理等场景，尤其适合需要复杂推理和全局一致性保证的任务。依赖预训练大模型和领域知识守卫，能在保证数据质量的基础上，减少资源消耗，提升系统鲁棒性。未来可扩展到多模态信息融合和自动守卫生成，推动神经符号一体化技术的实际应用，助力智能系统的可解释性和可扩展性。
Limitations & Outlook
假设oracle行为守卫尊重，若oracle输出偏差或不一致，可能影响最终结果的正确性。ASP推理在极复杂或关系密集场景中计算成本较高，影响实时性。守卫设计依赖领域知识，人工调优繁琐，自动化程度有限。未来需优化推理效率和守卫自动生成机制，以适应更复杂应用。
Plain Language Accessible to non-experts
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ELI14 Explained like you're 14
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Abstract
When Large Language Models (LLMs) are used for semantic data extraction from unstructured text, producing candidate relational facts from natural language, they may remain unreliable for tasks requiring complex combinatorial reasoning and global consistency. This paper proposes a logic-guided data extraction framework combining LLM-based extraction with Answer Set Programming (ASP). The LLM produces candidate facts, whereas ASP performs validation, inference, consistency checking, and control. Unlike existing pipelines that query the LLM independently for all target predicates, the proposed approach uses ASP reasoning to identify which predicates are logically admissible at each stage and to guide extraction queries. By interleaving LLM calls with ASP derivation, the framework infers logically implied facts without further extraction and detects inconsistencies early. We formalize the pipeline and prove that, under mild assumptions, it is equivalent to the baseline approach with respect to the final extracted facts, while requiring fewer LLM calls. We also introduce a caching mechanism for logic-based control queries, exploiting monotonicity of conjunctive queries over incrementally constructed fact sets to reduce solver invocations. Experiments on ASP-derived benchmarks show that the framework reduces LLM calls and improves extraction quality by mitigating spurious outputs, demonstrating the value of non-monotonic logic programming for controlled semantic extraction.