17 research outputs found
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
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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 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