提出首个面向一阶逻辑论证的多层级相似性框架，解决结构化推理匹配难题。
Technical Report -- A Context-Sensitive Multi-Level Similarity Framework for First-Order Logic Arguments: An Axiomatic Study
- 构建四层参数化模型，覆盖谓词到公式的逐级相似度计算
- 引入上下文加权机制，提升相似性判断的精细度与可解释性
- 适用于形式化论证聚合、隐含前提还原等逻辑推理任务
形式化论证中的相似性近年来受到关注，尤其在语义层面的论证聚合与隐含前提解码中具有重要意义。现有方法主要针对命题逻辑，而本文聚焦更丰富的**一阶逻辑（FOL）**场景，需考虑结构化内容的相似性。我们提出一个全面的FOL论证相似性框架，包含：(1) 扩展的公理基础；(2) 四层级参数化模型，涵盖谓词、文字、子句和公式层次的相似性；(3) 两种模型族，一种对语法敏感，均通过语言模型集成上下文权重，实现细粒度且可解释的相似性评估；(4) 形式化约束以保证期望性质。该框架为复杂逻辑推理任务提供坚实基础。
原文摘要 · Abstract (English)
Similarity in formal argumentation has recently gained attention due to its significance in problems such as argument aggregation in semantics and enthymeme decoding. While existing approaches focus on propositional logic, we address the richer setting of First-Order Logic (FOL), where similarity must account for structured content. We introduce a comprehensive framework for FOL argument similarity, built upon: (1) an extended axiomatic foundation; (2) a four-level parametric model covering predicates, literals, clauses, and formulae similarity; (3) two model families, one syntax-sensitive via language models, both integrating contextual weights for nuanced and explainable similarity; and (4) formal constraints enforcing desirable properties.
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