Which one is better: presentation-based or content-based math search?

Mathematical content is a valuable information source and retrieving this content has become an important issue. This paper compares two searching strategies for math expressions: presentation-based and content-based approaches. Presentation-based search uses state-of-the-art math search system while content-based search uses semantic enrichment of math expressions to convert math expressions into their content forms and searching is done using these content-based expressions. By considering the meaning of math expressions, the quality of search system is improved over presentation-based systems

A Survey on Retrieval of Mathematical Knowledge

We present a short survey of the literature on indexing and retrieval of mathematical knowledge, with pointers to 72 papers and tentative taxonomies of both retrieval problems and recurring techniques.Comment: CICM 2015, 20 page

MIaS: Math-Aware Retrieval in Digital Mathematical Libraries

Digital mathematical libraries (DMLs) such as arXiv, Numdam, and EuDML contain mainly documents from STEM fields, where mathematical formulae are often more important than text for understanding. Conventional information retrieval (IR) systems are unable to represent formulae and they are therefore ill-suited for math information retrieval (MIR). To fill the gap, we have developed, and open-sourced the MIaS MIR system. MIaS is based on the full-text search engine Apache Lucene. On top of text retrieval, MIaS also incorporates a set of tools for preprocessing mathematical formulae. We describe the design of the system and present speed, and quality evaluation results. We show that MIaS is both efficient, and effective, as evidenced by our victory in the NTCIR-11 Math-2 task

Semantic formula search in digital mathematical libraries

We are presenting semantic methods of search for mathematical objects in scientific publications. In particular, methods of search for mathematical formulas, as well as methods based on the logical structure of mathematical documents, are being discussed here. Based on the digital mathematical library Lobachevskii DML, created at Kazan Federal University in 2017, declared as Lobachevsky Year, we developed and tested new methods of search in digital collections of mathematical documents

Navegador ontológico matemático-NOMAT

The query algorithms in search engines use indexing, contextual analysis and ontologies, among other techniques, for text search. However, they do not use equations due to their writing complexity. NOMAT is a prototype of mathematical expression search engine that seeks information both in thesaurus and internet, using ontological tool for filtering and contextualizing information and LaTeX editor for the symbols in these expressions. This search engine was created to support mathematical research. Compared to other Internet search engines, NOMAT does not require prior knowledge of LaTeX, because has an editing tool which enables writing directly the symbols that make up the mathematical expression of interest. The results obtained were accurate and contextualized, compared to other commercial and no-commercial search engines.Los algoritmos de consulta de los motores de búsqueda utilizan indexación, análisis contextual y ontologías, entre otras técnicas, para la búsqueda de texto. Sin embargo, no utilizan ecuaciones debido a su complejidad de escritura. Nomat es un prototipo de motor de búsqueda de expresión matemática que busca información tanto en tesauro como en Internet, utilizando la Herramienta ontológica para filtrar y contextualizar información y editor de látex para los símbolos de estas expresiones. Este buscador fue creado para apoyar la investigación matemática. En comparación con otros motores de búsqueda de Internet, Nomat no requiere conocimientos previos de látex, ya que cuenta con una herramienta de edición que permite escribir directamente los símbolos que componen la expresión matemática de interés. Los resultados obtenidos fueron precisos y contextualizados, en comparación con otros motores de búsqueda comerciales y no comerciales

Mathematical Information Retrieval based on type embeddings and query expansion

We present an approach to mathematical information retrieval (MIR) that exploits a special kind of technical terminology, referred to as a mathematical type. In this paper, we present and evaluate a type detection mechanism and show its positive effect on the retrieval of research-level mathematics. Our best model, which performs query expansion with a type-aware embedding space, strongly outperforms standard IR models with state-of-the-art query expansion (vector space-based and language modelling-based), on a relatively new corpus of research-level queries

数学情報アクセスのための数式表現の検索と曖昧性解消

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 渋谷 哲朗, 東京大学教授 萩谷 昌己, 東京大学准教授 蓮尾 一郎, 東京大学准教授 鶴岡 慶雅, 東京工業大学准教授 藤井 敦University of Tokyo(東京大学

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

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 $P_{n}^{(\alpha, \beta)}\!\left(x\right)$ 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.Comment: Proceedings of The Web Conference 2020 (WWW'20), April 20--24, 2020, Taipei, Taiwa