通过语义感知缓存，让概念学习提速10倍
Semantics-Aware Caching for Concept Learning
- 用语义感知缓存替代重复查询，避免重复计算
- 在5个数据集上将检索和学习时间降低一个数量级
- 适用于符号与神经符号推理器，适合大规模知识学习
概念学习是一种在描述逻辑知识库上运行的监督机器学习方法。当前最先进的概念学习器通常需在可数无穷的概念空间中迭代搜索，每轮需调用实例检索以筛选最优候选概念。简单问题可能只需几十次调用，复杂问题则需上千次。本文提出一种语义感知缓存机制，本质上是一个子类关系感知的映射结构，通过清晰集合运算将概念与实例关联。在5个数据集上，使用4种符号推理器、1种神经符号推理器及5种常见分页策略的实验表明，该缓存可使概念检索与概念学习的运行时间降低一个数量级，且对符号与神经符号推理器均有效。
原文摘要 · Abstract (English)
Concept learning is a form of supervised machine learning that operates on knowledge bases in description logics. State-of-the-art concept learners often rely on an iterative search through a countably infinite concept space. In each iteration, they retrieve instances of candidate solutions to select the best concept for the next iteration. While simple learning problems might require a few dozen instance retrieval calls to find a fitting solution, complex learning problems might necessitate thousands of calls. We alleviate the resulting runtime challenge by presenting a semantics-aware caching approach. Our cache is essentially a subsumption-aware map that links concepts to a set of instances via crisp set operations. Our experiments on 5 datasets with 4 symbolic reasoners, a neuro-symbolic reasoner, and 5 popular pagination policies demonstrate that our cache can reduce the runtime of concept retrieval and concept learning by an order of magnitude while being effective for both symbolic and neuro-symbolic reasoners.
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