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International Conference on Intelligent Computer Mathematics

CICM 2015: Intelligent Computer Mathematics pp 243–257Cite as

Performance Evaluation and Optimization of Math-Similarity Search

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9150))

Abstract

Similarity search in math is to find mathematical expressions that are similar to a user’s query. We conceptualized the similarity factors between mathematical expressions, and proposed an approach to math similarity search (MSS) by defining metrics based on those similarity factors [11]. Our preliminary implementation indicated the advantage of MSS compared to non-similarity based search. In order to more effectively and efficiently search similar math expressions, MSS is further optimized. This paper focuses on performance evaluation and optimization of MSS. Our results show that the proposed optimization process significantly improved the performance of MSS with respect to both relevance ranking and recall.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The George Washington University, Washington, DC, 20052, USA

    Qun Zhang & Abdou Youssef

Authors
  1. Qun Zhang
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  2. Abdou Youssef
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Corresponding author

Correspondence to Qun Zhang .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham, United Kingdom

    Manfred Kerber

  2. McMaster University, Hamilton, Ontario, Canada

    Jacques Carette

  3. University of Innsbruck, Innsbruck, Austria

    Cezary Kaliszyk

  4. Jacobs University Bremen, Bremen, Germany

    Florian Rabe

  5. University of Birmingham, Birmingham, United Kingdom

    Volker Sorge

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© 2015 Springer International Publishing Switzerland

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Zhang, Q., Youssef, A. (2015). Performance Evaluation and Optimization of Math-Similarity Search. In: Kerber, M., Carette, J., Kaliszyk, C., Rabe, F., Sorge, V. (eds) Intelligent Computer Mathematics. CICM 2015. Lecture Notes in Computer Science(), vol 9150. Springer, Cham. https://doi.org/10.1007/978-3-319-20615-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-20615-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20614-1

  • Online ISBN: 978-3-319-20615-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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