让AI更聪明地查知识图谱，少查多想，准确率还高。
NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
- 用可微分符号推理+主动探索策略，智能选路径。
- 在标准数据集上准确率领先，查询次数减少40%以上。
- 适合需要精准多跳推理的问答系统开发者。
大型预训练语言模型和神经推理系统推动了诸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面前仍显不足。知识图谱提供了事实性知识的紧凑符号基础，但将图结构与神经模型结合颇具挑战：简单地将图事实嵌入提示会导致效率低下且脆弱；而纯符号或搜索密集型方法则耗费大量检索资源，缺乏梯度优化能力。本文提出NeuroSymActive，一个模块化框架，融合可微分神经-符号推理层与主动的价值引导探索控制器，用于知识图谱问答。该方法结合软合一式符号模块、神经路径评估器以及蒙特卡洛风格的探索策略，优先拓展高价值路径。在标准KGQA基准上的实验表明，NeuroSymActive在保持高准确率的同时，相比常见检索增强基线显著减少了昂贵的图查询和模型调用次数。
原文摘要 · Abstract (English)
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.
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