59 research outputs found
MDML: The Mathdoc Digital Mathematics Library
International audienceFollowing the steps of previous projects such as EuDML, Mathdoc is launching its Digital Mathematics Library. Based on a reliable infrastructure made for Numdam, learning from previous projects, and relying on a network of institutions we trust, we aim to push the ball further for accessing mathematical content online. We focus for a start on the aggregation part, aiming to reach a critical mass of mathematical content by harvesting various sources: OJS instances, preprint repositories , and locals DMLs. We thus build a database of mathematical documents, linking back to the source's website for accessing content
VMEXT: A Visualization Tool for Mathematical Expression Trees
Mathematical expressions can be represented as a tree consisting of terminal
symbols, such as identifiers or numbers (leaf nodes), and functions or
operators (non-leaf nodes). Expression trees are an important mechanism for
storing and processing mathematical expressions as well as the most frequently
used visualization of the structure of mathematical expressions. Typically,
researchers and practitioners manually visualize expression trees using
general-purpose tools. This approach is laborious, redundant, and error-prone.
Manual visualizations represent a user's notion of what the markup of an
expression should be, but not necessarily what the actual markup is. This paper
presents VMEXT - a free and open source tool to directly visualize expression
trees from parallel MathML. VMEXT simultaneously visualizes the presentation
elements and the semantic structure of mathematical expressions to enable users
to quickly spot deficiencies in the Content MathML markup that does not affect
the presentation of the expression. Identifying such discrepancies previously
required reading the verbose and complex MathML markup. VMEXT also allows one
to visualize similar and identical elements of two expressions. Visualizing
expression similarity can support support developers in designing retrieval
approaches and enable improved interaction concepts for users of mathematical
information retrieval systems. We demonstrate VMEXT's visualizations in two
web-based applications. The first application presents the visualizations
alone. The second application shows a possible integration of the
visualizations in systems for mathematical knowledge management and
mathematical information retrieval. The application converts LaTeX input to
parallel MathML, computes basic similarity measures for mathematical
expressions, and visualizes the results using VMEXT.Comment: 15 pages, 4 figures, Intelligent Computer Mathematics - 10th
International Conference CICM 2017, Edinburgh, UK, July 17-21, 2017,
Proceeding
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
Enabling mathematical cultures: introduction
Funder: Universität Hamburg (1037
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