发表机构
Georgia Tech（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SMTB是一种比SME快5-15倍且映射能力提升约50%的结构映射算法，通过最大化关系连接性而非偏向高阶关系，适用于任意关系图，并在5845个领域对上验证了其优越性。
AI 中文摘要
结构映射通过基于共享结构而非表面特征来对齐关系连接元素的系统，从而形成类比。我们提出了一种新的结构映射算法：具有紧边界的结构映射（SMTB），其速度比结构映射引擎（SME）快5至15倍，并且在大型嵌套领域中查找映射的能力约高出50%。SMTB是更广泛的认知规则引擎（CRE）项目的一部分，该项目是一个灵活的多语言兼容框架，具有可访问的Python接口，用于认知系统中常用的核心算法（如模式匹配、规划和结构映射）的最先进C++实现。CRE和SMTB旨在与广泛的表示选择兼容。与SME偏向于树状谓词逻辑中的高阶对应不同，SMTB在不优先考虑高阶关系的情况下最大化关系连接性。这使得SMTB在任意关系图上的表现与在嵌套谓词逻辑的树状领域中一样出色。我们讨论了在结构映射中优先考虑“高阶性”可能导致问题的情形，并说明了SMTB如何避免SME在这些情形下会遇到的失败模式。我们还提供了一项评估，将SMTB与SME v4在来自SME语料库的5845个领域对上进行了比较。
英文摘要
Structure-mapping forms analogies by aligning systems of relationally connected elements based on shared structure instead of surface features. We introduce a new structure-mapping algorithm: Structure-Mapping with Tight Bounds (SMTB) that is 5--15x faster than the structure-mapping engine (SME) and about 50\% better at finding mappings in large nested domains. SMTB is part of the broader Cognitive Rule Engine (CRE) project, a flexible multi-language-compatible framework with an accessible Python interface to state-of-the-art C++ implementations of core algorithms commonly used in cognitive systems such as pattern matching, planning, and structure-mapping. CRE and SMTB are designed to work with a wide range of representation choices. Unlike SME, which biases higher-order correspondences in tree-like predicate logic, SMTB maximizes relational connectivity without privileging higher-order relations. This allows SMTB to work just as well over arbitrary relational graphs as it does in tree-like domains of nested predicate logic. We discuss situations where privileging "higher-orderness" in structure-mapping can cause issues, and illustrate how SMTB avoids failure modes that SME would encounter in these situations. We also provide an evaluation comparing SMTB to SME v4 over 5845 domain pairs from the SME corpus.
