Discovering Mathematical Objects of Interest : A Study of Mathematical Notations

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2020
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Greiner-Petter, André
Müller, Fabian
Cohl, Howard
Aizawa, Akiko
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HUANG, Yennun, ed. and others. WWW '20: Proceedings of The Web Conference 2020. New York, NY: Association for Computing Machinery, 2020, pp. 1445-1456. ISBN 978-1-4503-7023-3. Available under: doi: 10.1145/3366423.3380218
Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache
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004 Informatik
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The Web Conference 2020, 20. Apr. 2020 - 24. Apr. 2020, Taipei, Taiwan
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ISO 690GREINER-PETTER, André, Moritz SCHUBOTZ, Fabian MÜLLER, Corinna BREITINGER, Howard COHL, Akiko AIZAWA, Bela GIPP, 2020. Discovering Mathematical Objects of Interest : A Study of Mathematical Notations. The Web Conference 2020. Taipei, Taiwan, 20. Apr. 2020 - 24. Apr. 2020. In: HUANG, Yennun, ed. and others. WWW '20: Proceedings of The Web Conference 2020. New York, NY: Association for Computing Machinery, 2020, pp. 1445-1456. ISBN 978-1-4503-7023-3. Available under: doi: 10.1145/3366423.3380218
BibTex
@inproceedings{GreinerPetter2020Disco-53714,
  year={2020},
  doi={10.1145/3366423.3380218},
  title={Discovering Mathematical Objects of Interest : A Study of Mathematical Notations},
  isbn={978-1-4503-7023-3},
  publisher={Association for Computing Machinery},
  address={New York, NY},
  booktitle={WWW '20: Proceedings of The Web Conference 2020},
  pages={1445--1456},
  editor={Huang, Yennun},
  author={Greiner-Petter, André and Schubotz, Moritz and Müller, Fabian and Breitinger, Corinna and Cohl, Howard and Aizawa, Akiko and Gipp, Bela}
}
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    <dcterms:abstract xml:lang="eng">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.&lt;br /&gt;&lt;br /&gt;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.</dcterms:abstract>
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