研究知识库推理的搜索空间结构如何影响效率
Growth Patterns of Inference
- 通过分析事实分布对推理性能的影响，构建搜索空间模型
- 均匀分布适合大知识库，偏斜分布更适合小知识库
- 发现问答性能存在突变点，指导新知识获取策略
什么类型的先验搜索空间特性有利于或阻碍推理？哪些事实最值得学习？回答这些问题对于理解演绎推理动态、构建高效的大规模知识驱动学习系统至关重要。本文通过建立模型，研究基础事实分布如何影响搜索空间中的推理表现。实验表明，在大规模知识库中，均匀分布的搜索空间表现更优；而在小规模知识库中，度分布偏斜的搜索空间性能更好。部分情况下观察到问答性能的显著突变，提示应基于现有知识的搜索空间结构来指导新事实的学习策略。
原文摘要 · Abstract (English)
What properties of a first-order search space support/hinder inference? What kinds of facts would be most effective to learn? Answering these questions is essential for understanding the dynamics of deductive reasoning and creating large-scale knowledge-based learning systems that support efficient inference. We address these questions by developing a model of how the distribution of ground facts affects inference performance in search spaces. Experiments suggest that uniform search spaces are suitable for larger KBs whereas search spaces with skewed degree distribution show better performance in smaller KBs. A sharp transition in Q/A performance is seen in some cases, suggesting that analysis of the structure of search spaces with existing knowledge should be used to guide the acquisition of new ground facts in learning systems.
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