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Missing Concept Extraction Using Rough Set Theory

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Proceedings of Fifth International Congress on Information and Communication Technology (ICICT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1183))

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Abstract

Ontology is used as knowledge representation of a particular domain that consists of the concepts and the two relations, namely taxonomic relation and non-taxonomic relation. In ontology, both relations are needed to give more knowledge about the domain texts, especially the non-taxonomic components that used to describe more about that domain. Most existing extraction methods extract the non-taxonomic relation component that exists in a same sentence with two concepts. However, there is a possibility of missing or unsure concept in a sentence, known as an incomplete sentence. It is difficult to identify the matching concepts in this situation. Therefore, this paper presents a method, namely similarity extraction method (SEM) to identify a missing concept in a non-taxonomic relation by using a rough set theory. The SEM will calculate the similarity precision and suggest as much as similar or relevant concepts to replace the missing or unclear value in an incomplete sentence. Data from the Tourism Corpus has been used for the experiment and the results were then evaluated by the domain experts. It is believed that this work is able to increase the pair extraction and thus enrich the domain texts.

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References

  1. Muzaffar, A.W., Azam, F., Qamar, U.: A relation extraction framework for biomedical text using hybrid feature set. Comput. Math. Methods Med. (2015)

    Google Scholar 

  2. Punuru, J., Chen, J.: Learning non-taxonomical semantic relations from domain texts. J. Intell. Inf. Syst. pp. 191–207 (2012)

    Google Scholar 

  3. Ribeiro, M.: Extraction of non-taxonomic relations from texts to enrich a basic ontology. Unpublished Master Thesis, School of Information Systems and Computer Engineering, University of Lisbon, Portugal (2014)

    Google Scholar 

  4. Serra, I., Girardi, R.: A Process for extracting non-taxonomic relationships of ontologies from text. Intell. Inf. Manage. 3(4), (2011)

    Google Scholar 

  5. Serra, I., Girardi, R., Novais, P.: PARNT: a statistic based approach to extract non-taxonomic relationships of ontologies from text. In: 2013 Tenth International Conference Information Technology: New Generations (ITNG), pp. 561–566. IEEE, New York (2013)

    Google Scholar 

  6. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)

    Article  Google Scholar 

  7. Kryszkiewicz, M.: Rough set approach to incomplete information systems. Inf. Sci. 112(1–4), 39–49 (1998)

    Article  MathSciNet  Google Scholar 

  8. Kryszkiewicz, M.: Rules in incomplete information systems. Inf. Sci. 113(3–4), 271–292 (1999)

    Article  MathSciNet  Google Scholar 

  9. Inukai, Y., Gehrmann, A., Nagai, Y., Ishizu, S.: Rough set theory using similarity of objects described by ontology. In: Proceedings of the 51st Annual Meeting of the ISSS-2007, Tokyo, Japan, vol. 51, no. 2 (2007)

    Google Scholar 

  10. Bi, Y., Anderson, T., McClean, S.: A rough set model with ontologies for discovering maximal association rules in document collections. Knowl.-Based Syst. pp. 243–251 (2003)

    Google Scholar 

  11. Ishizu, S., Gehrmann, A., Nagai, Y., Inukai, Y.: Rough ontology: extension of ontologies by rough sets. In: Symposium on Human Interface and the Management of Information, Springer, Berlin, Heidelberg, pp. 456–462 (2007)

    Google Scholar 

  12. Lin, D.: Dependency-based evaluation of MINIPAR. In: Treebanks, Springer, Netherlands, pp. 317–329 (2003)

    Google Scholar 

  13. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Record 22(2), 207–216 (1993)

    Article  Google Scholar 

  14. Deris, M.M., Abdullah, Z., Mamat, R., Yuan, Y.: A new limited tolerance relation for attribute selection in incomplete information systems. In: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 964–970. IEEE, New York (2015)

    Google Scholar 

  15. Porter, M.F.: An algorithm for suffix stripping. Program: Electron. Library Inf. Syst. 14(3), 130–137 (1980)

    Google Scholar 

  16. Uschold, M.: Creating, integrating and maintaining local and global ontologies. In: Proceedings of the First Workshop on Ontology Learning (OL-2000) in Conjunction with the 14th European Conference on Artificial Intelligence (ECAI-2000) (2000)

    Google Scholar 

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Acknowledgements

This work was supported under Fundamental Research Grant Scheme (FRGS) Grant, No: FRGS/1/2018/ICT04/USIM/03/1 and Universiti Sains Islam Malaysia (USIM).

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Authors and Affiliations

  1. Universiti Sains Islam Malaysia (USIM), 71800, Nilai, Negeri Sembilan, Malaysia

    N. F. Nabila, Nurlida Basir & Nurzi Juana Mohd Zaizi

  2. Universiti Tun Hussein Onn Malaysia, Batu Pahat, 86400, Johor, Malaysia

    Mustafa Mat Deris

Authors
  1. N. F. Nabila
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  2. Nurlida Basir
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  3. Nurzi Juana Mohd Zaizi
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  4. Mustafa Mat Deris
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Corresponding author

Correspondence to N. F. Nabila .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. Department of Biomedical Engineering, The University of Reading, Reading, UK

    R Simon Sherratt

  3. Department of Information Technology, Techno India Institute of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Nabila, N.F., Basir, N., Zaizi, N.J.M., Deris, M.M. (2021). Missing Concept Extraction Using Rough Set Theory. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Fifth International Congress on Information and Communication Technology. ICICT 2020. Advances in Intelligent Systems and Computing, vol 1183. Springer, Singapore. https://doi.org/10.1007/978-981-15-5856-6_46

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  • DOI: https://doi.org/10.1007/978-981-15-5856-6_46

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5855-9

  • Online ISBN: 978-981-15-5856-6

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