Technical Report -- A Context-Sensitive Multi-Level Similarity Framework for First-Order Logic Arguments: An Axiomatic Study
This work provides a foundational framework for similarity in FOL argumentation, addressing a known gap for researchers in formal argumentation and AI.
The paper introduces a multi-level similarity framework for First-Order Logic arguments, extending existing propositional approaches to handle structured content. The framework includes an axiomatic foundation, a four-level parametric model, and two model families with contextual weights.
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.