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JCR Impact Factor: 0.459
JCR 5-Year IF: 0.442
Issues per year: 4
Current issue: Feb 2017
Next issue: May 2017
Avg review time: 75 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  4/2014 - 15

Mathematical Formula Search using Natural Language Queries

YANG, S. See more information about YANG, S. on SCOPUS See more information about YANG, S. on IEEExplore See more information about YANG, S. on Web of Science, KO, Y. See more information about KO, Y. on SCOPUS See more information about KO, Y. on SCOPUS See more information about KO, Y. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (826 KB) | Citation | Downloads: 222 | Views: 1,191

Author keywords
information retrieval, formula search, MathML, natural language query, classification

References keywords
search(10), retrieval(10), information(8), math(7), digital(7), mathml(6), management(5), library(5), knowledge(5), kamali(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 99 - 104
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04015
Web of Science Accession Number: 000348772500015
SCOPUS ID: 84921625581

Abstract
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This paper presents how to search mathematical formulae written in MathML when given plain words as a query. Since the proposed method allows natural language queries like the traditional Information Retrieval for the mathematical formula search, users do not need to enter any complicated math symbols and to use any formula input tool. For this, formula data is converted into plain texts, and features are extracted from the converted texts. In our experiments, we achieve an outstanding performance, a MRR of 0.659. In addition, we introduce how to utilize formula classification for formula search. By using class information, we finally achieve an improved performance, a MRR of 0.690.


References | Cited By  «-- Click to see who has cited this paper

[1] M. E. Altamimi and A. Youssef, "A Math Query Language with an Expanded Set of Wildcards," Mathematics in Computer Science, vol. 2, no. 2, pp. 305-331, 2008.
[CrossRef] [SCOPUS Times Cited 10]


[2] A. Youssef, "Roles of Math Search in Mathematics," in Proc. Mathematical Knowledge Management, pp. 2-16, 2006.

[3] A. Youssef, "Relevance Ranking and Hit Description in Math Search," Mathematics in Computer Science, vol. 2, no. 2, pp. 333-353, 2008.
[CrossRef] [SCOPUS Times Cited 4]


[4] R. Miner and R. Munavalli, "An Approach to Mathematical Search Through Query Formulation and Data Normalization," in Proc. Mathematical Knowledge Management, pp.342-355, 2007.

[5] B. R. Miller and A. Youssef, "Augmenting Presentation MathML for Search," in Proc. Mathematical Knowledge Management, pp. 536-542, 2008.

[6] M. Adeel, H. S. Cheung and S. H. Khiyal, "Math GO! Prototype of A Content Based Mathematical Formula Search Engine," Journal of Theoretical and Applied Information Technology, vol. 4, no. 10, pp. 1002-1012, 2008.

[7] J. Misutka and L. Galambos, "Extending Full Text Search Engine for Mathematical Content," in Proc. Towards a Digital Mathematics Library, pp. 55-67, 2008.

[8] J. Zhao, M. Kan and Y. L. Theng, "Math Information Retrieval: User Requirements and Prototype Implementation," in Proc. Joint Conference on Digital Libraries, pp. 187-196, 2008.

[9] K. Yokoi and A. Aizawa, "An Approach to Similarity Search for Mathematical Expressions using MathML," in Proc. Towards a Digital Mathematics Library, pp. 27-35, 2009.

[10] P. D. F. Ion, "Mathematics and the World Wide Web," in Proc. Towards a Digital Mathematics Library, pp. 230-245, 2013.

[11] S, Kamali and F. W. Tompa, "Structural Similarity Search for Mathematics Retrieval," in Proc. Intelligent Computer Mathematics, pp. 246-262, 2013.

[12] S, Kamali and F. W. Tompa, "Retrieving Documents with Mathematical Content," in Proc. Special Interest Group on Information Retrieval, pp. 353-362, 2013.

[13] S. Kamali and F. W. Tompa, "A new mathematics retrieval system," in Proc. Conference on Information and Knowledge Management, pp. 1413-1416, 2010.

[14] S. Kamali and F. W. Tompa, "Improving Mathematics Retrieval," in Proc. Towards a Digital Mathematics Library, pp. 37-48, 2009.

[15] S. Kamali, J. Apacible and Y. Hosseinkashi, "Answering Math Queries with Search Engines," in Proc. companion on World Wide Web, pp. 43-52, 2012.

[16] R. Zanibbi and D. Blostein, "Recognition and Retrieval of Mathematical Expressions," International Journal on Document Analysis and Recognition, vol. 15, pp 331-357, 2012.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 65]


[17] M. Nghiem, G. Y. Kristianto and A. Aizawa, "Using MathML Parallel Markup Corpora for Semantic Enrichment of Mathematical Expressions," IEICE Transactions, vol. 96-D, no. 8, pp. 1707-1715, 2013.

[18] C. Do and E. J. Pauwels, "Using MathML to Represent Units of Measurement for Improved Ontology Alignment," in Proc. Towards a Digital Mathematics Library, pp. 310-325, 2013.

[19] M. Kohlhase, B. Matican and C. Prodescu, "MathWebSearch 0.5: Scaling an Open Formula Search Engine," in Proc. Artificial Intelligence and Symbolic Computation, pp. 342-357, 2012.

[20] M. Kohlhase and F. Rabe, "Semantics of OpenMath and MathML3," Mathematics in Computer Science, vol. 6, pp 235-260, 2012.
[CrossRef] [SCOPUS Times Cited 4]


[21] C. Lange, P. Ion, A. Dimou, C. Bratsas, W. Sperber, M. Kohlhase and I. Antoniou, "Bringing Mathematics to the Web of Data: The Case of the Mathematics Subject Classification," in Proc. European Semantic Web Symposium, pp. 763-777, 2012.

[22] P. Sojka and M. Liska, "Indexing and Searching Mathematics in Digital Libraries - Architecture, Design and Scalability Issues," in Proc. Mathematical Knowledge Management, pp. 228-243, 2011.

[23] P. Sojka and M. Liska, "The Art of Mathematics Retrieval," in Proc. ACM Symposium on Document Engineering, pp. 57-60, 2011.

[24] H. Ferreira and D. Freitas, "Audio-Math: Towards Automatic Readings of Mathematical Expressions," in Proc. Human Computer Interaction International, 2005.

[25] L. Liu and W. B. Croft, "Cluster-Based Retrieval Using Language Models," In Proc. Special Interest Group on Information Retrieval, pp. 186-193, 2004.

[26] Y. K. Jain and S. Wadekar, "Classification-based Retrieval Methods to Enhance Information Discovery on the Web," International Journal of Managing Information Technology, vol. 3, no. 1, pp. 33-44, 2011
[CrossRef]


[27] S. Kim, S. Yang and Y. Ko, "Classifying Mathematical Expressions Written in MathML," IEICE Transactions on Information and Systems, vol. E95-D, no. 10, pp. 2560-2563, 2012.

[28] P. Refaeilzadeh, L. Tang and H. Liu, "Cross-validation," Encyclopedia of Database Systems. pp. 532-538, 2009.



References Weight

Web of Science® Citations for all references: 39 TCR
SCOPUS® Citations for all references: 83 TCR

Web of Science® Average Citations per reference: 1 ACR
SCOPUS® Average Citations per reference: 3 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-05-22 15:24 in 35 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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