1,405
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Predicting Semantic Categories in Text Based on Knowledge Graph Combined with Machine Learning Techniques

ORCID Icon
Pages 933-951 | Received 04 May 2021, Accepted 06 Aug 2021, Published online: 19 Aug 2021

References

  • Abdelaal, H. M., B. R. Elemary, and H. A. Youness. 2019. Classification of hadith according to its content based on supervised learning algorithms. IEEE Access 7:152379–87. doi:https://doi.org/10.1109/ACCESS.2019.2948159.
  • Adeleke, A. O., N. A. Samsudin, A. Mustapha, N. Nawi et al. 2017. Comparative analysis of text classification algorithms for automated labelling of quranic verses. International Journal on Advanced Science, Engineering and Information Technology 7(4):1419. doi:https://doi.org/10.18517/ijaseit.7.4.2198.
  • Agrawal, R., T. Imieliński, and A. Swami. 1993. Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, Washington D.C. USA, 207–16.
  • AL-Kabi, Mohammed N., et al. 2015. Extended topical classification of hadith Arabic text. Int. J. Islam. Appl. Comput. Sci. Technol 3(3): 13-23.
  • Bahassine, S., A. Madani, M. Al-Sarem, M. Kissi et al. 2020. Feature selection using an improved Chi-square for Arabic text classification. Journal of King Saud University-Computer and Information Sciences 32(2):225–31. doi:https://doi.org/10.1016/j.jksuci.2018.05.010.
  • Bakar, M. Y. A., et al. 2018. Multi-label topic classification of hadith of Bukhari (Indonesian language translation) using information gain and backpropagation neural network. In: 2018 International Conference on Asian Language Processing (IALP), IEEE, Telkom University campus, Bandung, Indonesia, 344–50.
  • Dong, F., and Y. Zhang 2016. Automatic features for essay scoring–an empirical study. In: Proceedings of the 2016 conference on empirical methods in natural language processing, Austin, Texas, USA, 1072–77.
  • El-Fishawy, Nawal, et al. 2014. Arabic summarization in twitter social network. Ain Shams Engineering Journal 5(2): 411–420.
  • Elghazel, H., A. Aussem, O. Gharroudi, W. Saadaoui, et al. 2016. Ensemble multi-label text categorization based on rotation forest and latent semantic indexing. Expert Systems with Applications 57:1–11. doi:https://doi.org/10.1016/j.eswa.2016.03.041.
  • El-Khair, I. A. 2006. Effects of stop words elimination for Arabic information retrieval: A comparative study. International Journal of Computing & Information Sciences 4 (3):119–33.
  • Flachsbart, B., et al. 1994. Using the ID3 symbolic classification algorithm to reduce data density. In: Proceedings of the 1994 ACM symposium on Applied computing, Phoenix Arizona USA, 292–96.
  • Ghazizadeh, M., et al. 2008. Fuzzy expert system in determining Hadith 1 validity. In advances in computer and information sciences and engineering, Dordrecht: Springer, Azadi Square, Iran, 354–59.
  • Hajjar, M., et al. 2010. A system for evaluation of Arabic root extraction methods. In: 2010 Fifth International Conference on Internet and Web Applications and Services, IEEE, Barcelona, Spain, 506–12.
  • Harrag, F., and E. El-Qawasmah. 2009. Neural network for Arabic text classification. In: 2009 Second International Conference on the Applications of Digital Information and Web Technologies. IEEE, London, UK, 778–83.
  • Harrag, F., E. El-Qawasmeh, and P. Pichappan. 2009. Improving Arabic text categorization using decision trees. In: 2009 First International Conference on Networked Digital Technologies. IEEE, Ostrava, Czech Republic, 110–15.
  • Hassanat, A. B., et al. 2014, August. Solving the problem of the K parameter in the KNN classifier using an ensemble learning approach. arXiv preprint arXiv: 1409.0919
  • Kamsin, A., et al. 2014. Developing the novel Quran and Hadith authentication system. In: The 5th International Conference on Information and Communication Technology for The Muslim World (ICT4M), Kuching, Malaysia, IEEE, 1–5.
  • Maazouzi, F., and H. Bahi. 2012. Using multi decision tree technique to improving decision tree classifier. International Journal of Business Intelligence and Data Mining 7 (4):274–87. doi:https://doi.org/10.1504/IJBIDM.2012.051712.
  • Maraoui, H., K. Haddar, and L. Romary. 2018. Segmentation tool for hadith corpus to generate TEI encoding. In International Conference on Advanced Intelligent Systems and Informatics, 252–60. Cham: Springer.
  • Mosa, M. A. 2019a. Real-time data text mining based on Gravitational Search Algorithm. Expert Systems with Applications 137:117–29. doi:https://doi.org/10.1016/j.eswa.2019.06.065.
  • Mosa, M. A. 2020a. A novel hybrid particle swarm optimization and gravitational search algorithm for multi-objective optimization of text mining. Applied Soft Computing 90:106189. doi:https://doi.org/10.1016/j.asoc.2020a.106189.
  • Mosa, M. A. 2020b. Data text mining based on Swarm Intelligence Techniques: Review of text summarization systems. In A. Fiori (Eds.), Trends and Applications of Text Summarization Techniques (pp. 88-124). IGI Global. http://doi:10.4018/978-1-5225-9373-7.ch004
  • Mosa, M. A. 2017c June 5. How can ants extract the essence contents satellite of social networks. LAP LAMBERT Academic Publishing, 333032645X.
  • Mosa, M. A., A. Hamouda, and M. Marei. 2017a. Ant colony heuristic for user-contributed comments summarization. Knowledge-Based Systems 118:105–14. doi:https://doi.org/10.1016/j.knosys.2016.11.009.
  • Mosa, M. A., A. Hamouda, and M. Marei. 2017b. Graph coloring and ACO based summarization for social networks. Expert Systems with Applications 74:115–26. doi:https://doi.org/10.1016/j.eswa.2017.01.010.
  • Mosa, M. A., A. S. Anwar, and A. Hamouda. 2019b. A survey of multiple types of text summarization with their satellite contents based on swarm intelligence optimization algorithms. Knowledge-Based Systems 163:518–32. doi:https://doi.org/10.1016/j.knosys.2018.09.008.
  • Rostam, N. A. P., and N. H. A. H. Malim. 2019. Text categorisation in Quran and Hadith: Overcoming the interrelation challenges using machine learning and term weighting. Journal of King Saud University-Computer and Information Sciences 33: 658-667.
  • Saloot, M. A., N. Idris, R. Mahmud, S. Ja’afar, D. Thorleuchter, A. Gani et al. 2016. Hadith data mining and classification: A comparative analysis. Artificial Intelligence Review 46(1):113–28. doi:https://doi.org/10.1007/s10462-016-9458-x.
  • Suryana, N., F. S. Utomo, and A. Mohd Sanusi. 2018. quran ontology: Review on recent development and open research issues. Journal of Theoretical & Applied Information Technology 96: 3.
  • Zainol, Z., et al. 2016. Discovering “interesting” keyword patterns in Hadith chapter documents. In: 2016 International Conference on Information and Communication Technology (ICICTM), IEEE, 104–08, Vienna, Austria.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.