Abstract
This paper reports on the refinement of the algorithm for measuring terminological difference between text datasets (THD). This baseline THD algorithm, developed in the OntoElect project, used exact string matches for term comparison. In this work, it has been refined by the use of appropriately selected string similarity measures (SSM) for grouping the terms, which look similar as text strings and presumably have similar meanings. To determine rational term similarity thresholds for several chosen SSMs, the measures have been implemented as software functions and evaluated on the developed test set of term pairs in English. Further, the refined algorithm implementation has been evaluated against the baseline THD algorithm. For this evaluation, the bags of terms have been used that had been extracted from the three different document collections of scientific papers, belonging to different subject domains. The experiment revealed that the use of the refined THD algorithm, compared to the baseline, resulted in quicker terminological saturation on more compact sets of source documents, though at an expense of a noticeably higher computation time.
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Notes
- 1.
This paper is a refined and extended version of [1].
- 2.
- 3.
In this context, we do not distinguish a method and a measure. A method is understood as a way to implement the corresponding measure function.
- 4.
It is expected that Cosine measure, being based on the same principle as Jaccard, is not better than Jaccard in terms of performance, though takes more time to be computed.
- 5.
The test set and computed term similarity values are publicly available at https://github.com/OntoElect/Data/blob/master/STG/Test-Set.xls.
- 6.
The baseline THD algorithm is implemented in Python and is publicly available at https://github.com/OntoElect/Code/tree/master/THD.
- 7.
The functions for all the four selected SSMs have been implemented in Python 3.0 and return real values within [0, 1]. These functions are publicly available at: https://github.com/OntoElect/Code/tree/master/STG/core/methods.
- 8.
- 9.
- 10.
- 11.
https://github.com/OntoElect/Experiments/tree/master/STG. File names are {TIME, DMKD-300, DAC-cleaned}-Results-Alltogether-{min, ave, ave2, max, 1}-th.xlsx.
- 12.
These results could be accessed at https://github.com/OntoElect/Experiments/tree/master/STG. File names are {TIME, DMKD-300, DAC-cleaned}-Results-Alltogether-1-th.xlsx.
- 13.
The rank value is the sum of all ranks for a method within a particular threshold in Table 6. The highest rank value indicates the lowest performance. For case (a) it equals to 12, which is for Sørensen-Dice at Min threshold. For case (b) it equals to 4.
- 14.
Which should be so as the baseline THD does not group terms. Hence, any alternative method that does similar terms grouping retains less significant terms.
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Acknowledgements
The research leading to this publication has been performed in part in cooperation between the Department of Computer Science of Zaporizhzhia National University, the Ontology Engineering Group of the Universidad Politécnica de Madrid, the Applied Probability and Informatics Department at the RUDN University, and Springer-Verlag GmbH. The first author is funded by a PhD grant awarded by Zaporizhzhia National University and the Ministry of Education and Science of Ukraine. The second author is supported by the FPI grant (BES-2017-082511) under the DATOS 4.0: RETOS Y SOLUCIONES - UPM project (TIN2016-78011-C4-4-R) funded by Ministerio de Economía, Industria y Competitividad of Spanish government and EU FEDER funds. The fourth author acknowledges the support of the “RUDN University Program 5-100”. The authors would like to acknowledge the contributions by Alyona Chugunenko and Rodion Popov for their research contributions leading to this publication. In particular, they helped develop the approach for term grouping and implement the software for it. The collection of full text Springer journal papers dealing with Knowledge Management, including DMKD-300, has been provided by Springer-Verlag. The authors would also like to express their gratitude to anonymous reviewers whose comments and suggestions helped improve the paper.
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Kosa, V., Chaves-Fraga, D., Keberle, N., Birukou, A. (2019). Similar Terms Grouping Yields Faster Terminological Saturation. In: Ermolayev, V., Suárez-Figueroa, M., Yakovyna, V., Mayr, H., Nikitchenko, M., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol 1007. Springer, Cham. https://doi.org/10.1007/978-3-030-13929-2_3
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