# Discovering Mathematical Objects of Interest -- A Study of Mathematical Notations

arXiv: https://arxiv.org/abs/2002.02712 · HTML: https://mlsanity.com/paper/2002.02712/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Neural Machine Translation for Mathematical Formulae](https://mlsanity.com/paper/2305.16433/) (2023)
- [Discovery and Recognition of Formula Concepts using Machine Learning](https://mlsanity.com/paper/2303.01994/) (2023)
- [A Survey on Semantics in Automated Data Science](https://mlsanity.com/paper/2205.08018/) (2022)
- [A Vision for Semantically Enriched Data Science](https://mlsanity.com/paper/2303.01378/) (2023)

## Abstract (arXiv)

Mathematical notation, i.e., the writing system used to communicate concepts in mathematics, encodes valuable information for a variety of information search and retrieval systems. Yet, mathematical notations remain mostly unutilized by today's systems. In this paper, we present the first in-depth study on the distributions of mathematical notation in two large scientific corpora: the open access arXiv (2.5B mathematical objects) and the mathematical reviewing service for pure and applied mathematics zbMATH (61M mathematical objects). Our study lays a foundation for future research projects on mathematical information retrieval for large scientific corpora. Further, we demonstrate the relevance of our results to a variety of use-cases. For example, to assist semantic extraction systems, to improve scientific search engines, and to facilitate specialized math recommendation systems. The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e.g., linking Pn(α, β)!left(xright) with `Jacobi polynomial'); (3) we extend zbMATH's search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems. To expedite future research projects, we have made available our source code and data.