Similar Terms Grouping Yields Faster Terminological Saturation

  • Victoria KosaEmail author
  • David Chaves-Fraga
  • Nataliya Keberle
  • Aliaksandr Birukou
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1007)


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.


Automated term extraction OntoElect Terminological difference String similarity measure Bag of terms Terminological saturation 



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.


  1. 1.
    Chugunenko, A., Kosa, V., Popov, R., Chaves-Fraga, D., Ermolayev, V.: Refining terminological saturation using string similarity measures. In: Ermolayev, V., et al. (eds.) Proceedings of the ICTERI 2018. Volume I: Main Conference, Kyiv, Ukraine, 14–17 May 2018, vol. 2105, pp. 3–18. CEUR-WS, onlineGoogle Scholar
  2. 2.
    Tatarintseva, O., Ermolayev, V., Keller, B., Matzke, W.-E.: Quantifying ontology fitness in ontoelect using saturation- and vote-based metrics. In: Ermolayev, V., Mayr, H.C., Nikitchenko, M., Spivakovsky, A., Zholtkevych, G. (eds.) ICTERI 2013. CCIS, vol. 412, pp. 136–162. Springer, Cham (2013). Scholar
  3. 3.
    Ermolayev, V.: OntoElecting requirements for domain ontologies. The case of time domain. EMISA Int. J. Concept. Model. 13(Sp. Issue), 86–109 (2018)Google Scholar
  4. 4.
    Fahmi, I., Bouma, G., van der Plas, L.: Improving statistical method using known terms for automatic term extraction. In: Computational Linguistics in the Netherlands, CLIN 17 (2007)Google Scholar
  5. 5.
    Wermter, J., Hahn, U.: Finding new terminology in very large corpora. In: Clark, P., Schreiber, G. (eds.) Proceedings of the 3rd International Conference on Knowledge Capture, K-CAP 2005, pp. 137–144. ACM, Banff (2005)Google Scholar
  6. 6.
    Zhang, Z., Iria, J., Brewster, C., Ciravegna, F.: A comparative evaluation of term recognition algorithms. In: Proceedings of the 6th International Conference on Language Resources and Evaluation, LREC 2008, Marrakech, Morocco (2008)Google Scholar
  7. 7.
    Daille, B.: Study and implementation of combined techniques for automatic extraction of terminology. In: Klavans, J., Resnik, P. (eds.) The Balancing Act: Combining Symbolic and Statistical Approaches to Language, pp. 49–66. The MIT Press, Cambridge (1996)Google Scholar
  8. 8.
    Caraballo, S.A., Charniak, E.: Determining the specificity of nouns from text. In: Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 63–70 (1999)Google Scholar
  9. 9.
    Astrakhantsev, N.: ATR4S: toolkit with state-of-the-art automatic terms recognition methods in scala. arXiv preprint arXiv:1611.07804 (2016)
  10. 10.
    Medelyan, O., Witten, I.H.: Thesaurus based automatic keyphrase indexing. In: Marchionini, G., Nelson, M.L., Marshall, C.C. (eds.) Proceedings of the ACM/IEEE Joint Conf on Digital Libraries, JCDL 2006, pp. 296–297. ACM, Chapel Hill (2006)Google Scholar
  11. 11.
    Ahmad, K., Gillam, L., Tostevin, L.: University of surrey participation in TREC8: weirdness indexing for logical document extrapolation and retrieval (WILDER). In: Proceedings of the 8th Text Retrieval Conference, TREC-8 (1999)Google Scholar
  12. 12.
    Sclano, F., Velardi, P.: TermExtractor: a web application to learn the common terminology of interest groups and research communities. In: Proceedings of the 9th Conference on Terminology and Artificial Intelligence, TIA 2007, Sophia Antipolis, France (2007)Google Scholar
  13. 13.
    Frantzi, K.T., Ananiadou, S.: The C/NC value domain independent method for multi-word term extraction. J. Nat. Lang. Proc. 6(3), 145–180 (1999)CrossRefGoogle Scholar
  14. 14.
    Kozakov, L., Park, Y., Fin, T., Drissi, Y., Doganata, Y., Cofino, T.: Glossary extraction and utilization in the information search and delivery system for IBM Technical Support. IBM Syst. J. 43(3), 546–563 (2004)CrossRefGoogle Scholar
  15. 15.
    Astrakhantsev, N.: Methods and software for terminology extraction from domain-specific text collection. Ph.D. thesis, Institute for System Programming of Russian Academy of Sciences (2015)Google Scholar
  16. 16.
    Bordea, G., Buitelaar, P., Polajnar, T.: Domain-independent term extraction through domain modelling. In: Proceedings of the 10th International Conference on Terminology and Artificial Intelligence, TIA 2013, Paris, France (2013)Google Scholar
  17. 17.
    Badenes-Olmedo, C., Redondo-García, J.L., Corcho, O.: Efficient clustering from distributions over topics. In: Proceedings of the K-CAP 2017, Article 17, 8 p. ACM, New York (2017)Google Scholar
  18. 18.
    Gomaa, W.H., Fahmy, A.A.: A survey of text similarity approaches. Int. J. Comput. Appl. 68(13), 13–18 (2013)Google Scholar
  19. 19.
    Yu, M., Li, G., Deng, D., Feng, J.: String similarity search and join: a survey. Front. Comput. Sci. 10(3), 399–417 (2016)CrossRefGoogle Scholar
  20. 20.
    Miller, G.A., Beckwith, R., Fellbaum, C.D., Gross, D., Miller, K.: WordNet: an online lexical database. Int. J. Lexicograph. 3(4), 235–244 (1990)CrossRefGoogle Scholar
  21. 21.
    Arnold, M., Ohlebusch, E.: Linear time algorithms for generalizations of the longest common substring problem. Algorithmica 60(4), 806–818 (2011)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)MathSciNetGoogle Scholar
  23. 23.
    Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Monger, A., Elkan, C.: The field-matching problem: algorithm and applications. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 267–270. AAAI Press (1996)Google Scholar
  25. 25.
    Jaro, M.A.: Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J. Am. Stat. Assoc. 84(406), 414–420 (1989)CrossRefGoogle Scholar
  26. 26.
    Winkler, W.E.: String comparator metrics and enhanced decision rules in the Fellegi-Sunter model of record linkage. In: Proceedings of the Section on Survey Research Methods. ASA, pp. 354–359 (1990)Google Scholar
  27. 27.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar
  28. 28.
    Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Kongelige Danske Videnskabernes Selskab 5(4), 1–34 (1948)Google Scholar
  29. 29.
    Jaccard, P.: The distribution of the flora in the alpine zone. New Phytol. 11, 37–50 (1912)CrossRefGoogle Scholar
  30. 30.
    Huang, A.: Similarity measures for text document clustering. In: Proceedings of the 6th New Zealand Computer Science Research Student Conference (NZCSRSC2008), Christchurch, New Zealand, pp. 49–56 (2008)Google Scholar
  31. 31.
    Singhal, A.: Modern information retrieval: a brief overview. Bull. the IEEE Comput. Soc. Tech. Comm. Data Eng. 24(4), 35–43 (2001)Google Scholar
  32. 32.
    Lu, J., Lin, C., Wang, W., Li, C., Wang, H.: String similarity measures and joins with synonyms. In: Proceedings of the 2013 ACM SIGMOD International Conference on the Management of Data, pp. 373–384 (2013)Google Scholar
  33. 33.
    Lee, H., Ng, R.T., Shim, K.: Power-law based estimation of set similarity join size. Proc. VLDB Endow. 2(1), 658–669 (2009)CrossRefGoogle Scholar
  34. 34.
    Tsuruoka, Y., McNaught, J., Tsujii, J., Ananiadou, S.: Learning string similarity measures for gene/protein name dictionary look-up using logistic regression. Bioinformatics 23(20), 2768–2774 (2007)CrossRefGoogle Scholar
  35. 35.
    Qin, J., Wang, W., Lu, Y., Xiao, C., Lin, X.: Efficient exact edit similarity query processing with the asymmetric signature scheme. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 1033–1044. ACM, New York (2011)Google Scholar
  36. 36.
    Corcho, O., Gonzalez, R., Badenes, C., Dong, F.: Repository of indexed ROs. Deliverable No. 5.4. Dr Inventor project (2015)Google Scholar
  37. 37.
    Kosa, V., et al.: Cross-evaluation of automated term extraction tools by measuring terminological saturation. In: Bassiliades, N., et al. (eds.) ICTERI 2017. CCIS, vol. 826, pp. 135–163. Springer, Cham (2018). Scholar
  38. 38.
    Minkowski, H.: Geometrie der Zahlen. Bibliotheca Mathematica Teubneriana, Band 40 Johnson Reprint Corp., New York-London, 256 pp. (1968). (in German)Google Scholar
  39. 39.
    Moiseenko, S., Ermolayev, V.: Conceptualizing and formalizing requirements for ontology engineering. In: Antoniou, G., Zholtkevych, G. (eds.) Proceedings of the ICTERI 2018 Ph.D. Symposium, Kyiv, Ukraine, 14–17 May, vol. 2122, pp. 35–44. CEUR-WS (2018, online)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceZaporizhzhia National UniversityZaporizhzhiaUkraine
  2. 2.Ontology Engineering GroupUniversidad Politécnica de MadridMadridSpain
  3. 3.Springer-Verlag GmbHHeidelbergGermany
  4. 4.Peoples’ Friendship University of Russia (RUDN University)MoscowRussia

Personalised recommendations