# An Overview of Distance and Similarity Functions for Structured Data

arXiv: https://arxiv.org/abs/2002.07420 · HTML: https://mlsanity.com/paper/2002.07420/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 10 of 10)

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- [Robot Learning from Demonstration Using Elastic Maps](https://mlsanity.com/paper/2208.02207/) (2022)
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## Abstract (arXiv)

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.