The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations. While distance functions for propositional representations have been thoroughly studied, work on distance functions for structured representations, such as graphs, frames or logical clauses, has been carried out in different communities and is much less understood. Specifically, a significant amount of work that requires the use of a distance or similarity function for structured representations of data usually employs ad-hoc functions for specific applications. Therefore, the goal of this paper is to provide an overview of this work to identify connections between the work carried out in different areas and point out directions for future work.

中文翻译：

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结构化数据的距离和相似度函数概述

距离和相似性的概念在许多机器学习方法和一般的人工智能中起着关键作用，因为它们可以作为个人对对象进行分类、形成概念和进行概括的组织原则。虽然已经彻底研究了命题表示的距离函数，但结构化表示（例如图、框架或逻辑子句）的距离函数的工作已在不同的社区中进行，但鲜为人知。具体来说，需要使用距离或相似度函数来表示数据的结构化表示的大量工作通常会针对特定应用程序使用 ad-hoc 函数。所以，