Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowledge-intensive queries that require precise, structured multi-hop inference. Knowledge graphs provide a compact symbolic substrate for factual grounding, but integrating graph structure with neural models is nontrivial: naively embedding graph facts into prompts leads to inefficiency and fragility, while purely symbolic or search-heavy approaches can be costly in retrievals and lack gradient-based refinement. We introduce NeuroSymActive, a modular framework that combines a differentiable neural-symbolic reasoning layer with an active, value-guided exploration controller for Knowledge Graph Question Answering. The method couples soft-unification style symbolic modules with a neural path evaluator and a Monte-Carlo style exploration policy that prioritizes high-value path expansions. Empirical results on standard KGQA benchmarks show that NeuroSymActive attains strong answer accuracy while reducing the number of expensive graph lookups and model calls compared to common retrieval-augmented baselines.

翻译：大型预训练语言模型与神经推理系统已推动众多自然语言任务的发展，但在需要精确、结构化多跳推理的知识密集型查询任务中仍面临挑战。知识图谱为事实性基础提供了紧凑的符号化载体，但将图结构与神经模型相结合并非易事：简单地将图谱事实嵌入提示会导致效率低下与系统脆弱性，而纯符号化或强依赖搜索的方法则存在检索成本高昂且缺乏基于梯度的优化能力的问题。本文提出NeuroSymActive——一个面向知识图谱问答的模块化框架，它结合了可微分神经符号推理层与基于价值引导的主动探索控制器。该方法将软统一风格的符号模块与神经路径评估器、以及优先扩展高价值路径的蒙特卡洛式探索策略相耦合。在标准KGQA基准测试上的实证结果表明，相较于常见的检索增强基线方法，NeuroSymActive在保持较强答案准确率的同时，显著减少了昂贵的图谱查询与模型调用次数。