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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Towards semantic search for mathematical notation

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DOI: http://dx.doi.org/10.15439/2018F155

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 465–469 ()

Full text

Abstract. The paper concerns the design and implementation of a search engine for mathematical expressions given by the user in a convenient form of spoken or visual queries. Proper presentation and transcription of the mathematical notation is substantial for further processing and the adequate choice of the word distance measure for string comparison is an important issue as well. Within this project a complete solution for acquiring and processing the mathematical query and a searching algorithm is elaborated. We present results of exemplary search queries obtained for different types of input data format with application of two different word distance measures and discuss briefly the observed properties.

References

  1. L. A. Chang, Handbook for Spoken Mathematics (Larry’s Speakeasy). Lawrence Livermore Laboratory, The Regents of the University of California, 1983.
  2. R. Fateman, “Handwriting + speech for computer entry of mathematics,” Style, Benjamin L. Kovitz, Manning Publications Company, 2004.
  3. R. Fateman, “How can we speak math?” Computer Science Division, EECS Department, University of California at Berkeley, Tech. Rep., 2013.
  4. J. Cuartero-Olivera, G. Hunter, and A. Pérez-Navarro, “Reading and writing mathematical notation in e-learning environments,” eLearn Center Research Paper Series, no. 4, pp. 11–20, 2012.
  5. Z. Sroczyński, “Priority levels and heuristic rules in the structural recognition of mathematical formulae,” Theoretical and Applied Informatics, vol. 22, no. 4, p. 273, 2010.
  6. A. Bier and Z. Sroczyński, “Adaptive math-to-speech interface,” in Proceedings of the Mulitimedia, Interaction, Design and Innnovation, ser. MIDI ’15. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2814464.2814471. ISBN 978-1-4503-3601-7 pp. 7:1–7:9. [Online]. Available: http://doi.acm.org/10.1145/2814464.2814471
  7. D. Attanayake, J. Denholm-Price, G. Hunter, E. Pfluegel, and A. Wigmore, “Intelligent assistive interfaces for editing mathematics.” in Intelligent Environments (Workshops), 2012, pp. 286–297.
  8. D. Attanayake, G. Hunter, J. Denholm-Price, and E. Pfluegel, “Novel multi-modal tools to enhance disabled and distance learners’ experience of mathematics,” ICTer, vol. 6, no. 1, 2013.
  9. T. Sancho-Vinuesa, C. Córcoles, M. Huertas, A. Pérez-Navarro, D. Marquès, R. Eixarch, and J. Villalonga, “Automatic verbalization of mathematical formulae for web-based learning resources in an on-line environment,” INTED2009 Proceedings, pp. 4312–4321, 2009.
  10. M. Maćkowski, P. Brzoza, M. Żabka, and D. Spinczyk, “Multimedia platform for mathematics’ interactive learning accessible to blind people,” Multimedia Tools and Applications, pp. 1–18, 2017. http://dx.doi.org/10.1007/s11042-017-4526-z
  11. I. Kohanova, “The ways of teaching mathematics to visually impaired students,” in International Congress on Mathematical Education (ICME), 2008.
  12. S. Yang and Y. Ko, “Mathematical formula search using natural language queries,” Advances in Electrical and Computer Engineering, vol. 14, no. 4, pp. 99–104, 2014. http://dx.doi.org/10.4316/AECE.2014.04015
  13. M. Líška, P. Sojka, and M. Ružicka, “Similarity search for mathematics: Masaryk university team at the ntcir-10 math task,” in Proceedings of the 10th NTCIR Conference on Evaluation of Information Access Technologies. Citeseer, 2013, pp. 686–691.
  14. M. Líška, P. Sojka, and M. Rŭžička, “Math indexer and searcher web interface,” in International Conference on Intelligent Computer Mathematics. Springer, 2014. http://dx.doi.org/10.1007/978-3-319-08434-3_36 pp. 444–448.
  15. J. Mišutka and L. Galamboš, “Extending full text search engine for mathematical content,” Towards Digital Mathematics Library. Birmingham, United Kingdom, July 27th, 2008, pp. 55–67, 2008.
  16. P. Sojka and M. Líška, “The art of mathematics retrieval,” in Proceedings of the 11th ACM symposium on Document engineering. ACM, 2011. http://dx.doi.org/10.1145/2034691.2034703 pp. 57–60.
  17. M. Adeel, M. Sher, and M. S. H. Khiyal, “Efficient cluster-based information retrieval from mathematical markup documents,” World Applied Sciences Journal, vol. 17, no. 5, pp. 611–616, 2012. http://dx.doi.org/10.13140/2.1.5166.6562
  18. M. N. Quoc, K. Yokoi, Y. Matsubayashi, and A. Aizawa, “Mining coreference relations between formulas and text using wikipedia,” in 23rd International Conference on Computational Linguistics, 2010, p. 69.
  19. D. Formánek, M. Líška, M. Rŭžička, and P. Sojka, “Normalization of digital mathematics library content,” in Joint Proceedings of the 24 th Workshop on OpenMath and the 7 th Workshop on Mathematical User Interfaces (MathUI), 2012, p. 91.
  20. P.-Y. Chien and P.-J. Cheng, “Semantic tagging of mathematical expressions,” in Proceedings of the 24th International Conference on World Wide Web. ACM, 2015. http://dx.doi.org/10.1145/2736277.2741108 pp. 195–204.
  21. M. E. Altamimi and A. S. Youssef, “Wildcards in math search, implementation issues.” in CAINE. Citeseer, 2007, pp. 90–96.
  22. A. Niewiarowski and M. Stanuszek, “The mechanism of identification and classification of content (in Polish),” Studia Informatica, vol. 34, no. 2B, pp. 205–222, 2013.
  23. D. Połap, “Neural validation of grammatical correctness of sentences,” Ceur-ws, 2016.
  24. S. Kozielski, M. Świderski, and M. Bach, “The use of natural language as an intuitive semantic integration system interface,” in Internet–Technical Development and Applications. Springer, 2009, pp. 51–58.
  25. J. Jagielski and P. Wnęk, “Natural language in databases systems,” Studia Informatica, vol. 31, no. 2B, pp. 281–290, 2010.
  26. F. Li and H. Jagadish, “Constructing an interactive natural language interface for relational databases,” Proceedings of the VLDB Endowment, vol. 8, no. 1, pp. 73–84, 2014. http://dx.doi.org/10.14778/2735461.2735468
  27. R. Alexander, P. Rukshan, and S. Mahesan, “Natural language web interface for database (nlwidb),” arXiv preprint https://arxiv.org/abs/1308.3830, 2013.
  28. G. Navarro, “A guided tour to approximate string matching,” ACM Comput. Surv., vol. 33, no. 1, pp. 31–88, Mar. 2001. http://dx.doi.org/10.1145/375360.375365.