论文标题
结构化数据的距离和相似性功能的概述
An Overview of Distance and Similarity Functions for Structured Data
论文作者
论文摘要
距离和相似性的概念在许多机器学习方法和人工智能（AI）中起着关键作用，因为它们可以作为一个组织原则，个人可以通过该原则对对象进行分类，形成概念并进行概括。虽然已经对命题表示的距离函数进行了彻底的研究，但在不同社区中已经进行了结构化表示的距离函数（例如图形，框架或逻辑从句）的工作，并且不了解。具体而言，需要使用距离或相似性函数来完成数据的结构化表示，通常采用临时功能来实现特定应用程序。因此，本文的目的是概述这项工作，以确定在不同领域进行的工作之间的联系并指出未来工作的指示。
The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence (AI) 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.
