A Robust Random Search Approach for Matching Formulas in Math Information Retrieval Systems

Math is a major contributor to many areas of study, and gives someone skills that (s)he can use across other subjects and different job roles. Unfortunately, a recent study from the National Assessment of Educational Progress shows that no more than 26% of 12<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> graders in the USA have been rated proficient in math since 2005, and COVID-19 only made the situation worse. In principle, appropriate online searching could promote learning of individual math concepts to help surmount the learning gap. In practice, however, current online searching works poorly for math. While traditional information retrieval systems identify semantically related documents outside of math, such systems were not designed for handling math formulas. Although some work has been done on Mathematical Information Retrieval (MIR) recently, little has focused specifically on developing indexing schemas to quickly search for and retrieve math formulas contained within math questions and answers. The objective of indexing symbols and notations used in math equations is to organize and categorize math information in a way that makes it easier to retrieve and access relevant answers to math questions. To achieve this objective, we propose a robust random search approach for retrieving math information, offering an optimal solution to speed up the process of searching huge volume of math archive. Our design goals of indexing math equations include fast matching math answers to questions, reducing disk input/output, and optimizing the process of solving math questions with suitable answers by enhancing its processing speed.