A Proposed Approach for Arabic Semantic Annotation

  • Ghada KhairyEmail author
  • A. A. Ewees
  • Mohamed Eisa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


Semantic annotation refers to the process of annotating documents using the ontology in order to data becomes meaningful. Most of the techniques and methods of the field of semantic annotation and retrieval are used for dealing broadly in the English language. This paper aims to enhance the process of information retrieval for Arabic language that depends on the ontology in the process of document annotation. To achieve this aim, it is determined and processed the problems of the Arabic language through the proposed approach. This paper depends on semantic annotation based on ontology and Resource Description Framework (RDF). The results achieved high precision and high recall for the semantic annotation based on the proposed approach.


Arabic semantic annotation Ontology Resource Description Framework Stemming 


  1. 1.
    Al-Bukhitan, S., Helmy, T., Al-Mulhem, M.: Semantic annotation tool for annotating Arabic web documents. Procedia Comput. Sci. 32, 429–436 (2014)CrossRefGoogle Scholar
  2. 2.
    Oliveira, P., Rocha, J.: Semantic annotation tools survey. In: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 301–307. IEEE, April 2013Google Scholar
  3. 3.
    Beseiso, M., Ahmad, A.R., Ismail, R.: A Survey of Arabic language support in semantic web. Int. J. Comput. Appl. 9(1), 35–40 (2010)Google Scholar
  4. 4.
    Kaloub, A.: Automatic ontology-based document annotation for Arabic information retrieval. Unpublished master’s thesis, Islamic University-Gaza, Deanery of Graduate Studies, Faculty of Information Technology (2013)Google Scholar
  5. 5.
    El Aziz, M.A., Ewees, A.A., Hassanien, A.E.: Hybrid swarms optimization based image segmentation. In: Hybrid Soft Computing for Image Segmentation, pp. 1–21 (2016)Google Scholar
  6. 6.
    El Aziz, M.A., Ewees, A.A., Hassanien, A.E., Mudhsh, M., Xiong, S.: Multi-objective whale optimization algorithm for multilevel thresholding segmentation. In: Advances in Soft Computing and Machine Learning in Image Processing, pp. 23–39 (2018)Google Scholar
  7. 7.
    El Aziz, M.A., Ewees, A.A., Hassanien, A.E.: Multi-objective whale optimization algorithm for content-based image retrieval. Multimed. Tools Appl. 77(19), 26135–26172 (2018)CrossRefGoogle Scholar
  8. 8.
    El Aziz, M.A., Ewees, A.A., Hassanien, A.E.: Whale optimization algorithm and Moth-Flame optimization for multilevel thresholding image segmentation. Expert Syst. Appl. 83, 242–256 (2017)CrossRefGoogle Scholar
  9. 9.
    Sahlol, A.T., Moemen, Y.S., Ewees, A.A., Hassanien, A.E.: Evaluation of cisplatin efficiency as a chemotherapeutic drug based on neural networks optimized by genetic algorithm. In: 2017 12th International Conference on Computer Engineering and Systems (ICCES), pp. 682–685. IEEE, December 2017Google Scholar
  10. 10.
    Ahmed, K., Ewees, A.A., Hassanien, A.E.: Prediction and management system for forest fires based on hybrid flower pollination optimization algorithm and adaptive neuro-fuzzy inference system. In: 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 299–304, December 2017Google Scholar
  11. 11.
    Sahlol, A.T., Ewees, A.A., Hemdan, A.M., Hassanien, A.E.: Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite. In: 2016 12th International Computer Engineering Conference (ICENCO), pp. 35–40. IEEE, December 2016Google Scholar
  12. 12.
    Oliva, D., Ewees, A.A., Aziz, M.A., Hassanien, A., Peréz-Cisneros, M.: A chaotic improved artificial bee colony for parameter estimation of photovoltaic cells. Energies 10(7), 865 (2017)CrossRefGoogle Scholar
  13. 13.
    Ahmed, K., Ewees, A.A., El Aziz, M.A., Hassanien, A.E., Gaber, T., Tsai, P.W., Pan, J.S.: A hybrid krill-ANFIS model for wind speed forecasting. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 365–372, October 2016Google Scholar
  14. 14.
    Ewees, A.A., El Aziz, M.A., Hassanien, A.E.: Chaotic multi-verse optimizer-based feature selection. Neural Comput. Appl., 1–16 (2017)Google Scholar
  15. 15.
    Al-Yahya, M., Al-Shaman, M., Al-Otaiby, N., Al-Sultan, W., Al-Zahrani, A., Al-Dalbahie, M.: Ontology-based semantic annotation of Arabic language text. Int. J. Mod. Educ. Comput. Sci. 7(7), 53 (2015)CrossRefGoogle Scholar
  16. 16.
    El-ghobashy, A.N., Attiya, G.M., Kelash, H.M.: A proposed framework for Arabic semantic annotation tool. Int. J. Com. Dig. Syst. 3(1), 47–53 (2014)CrossRefGoogle Scholar
  17. 17.
    Albukhitan, S., Helmy, T.: Automatic ontology-based annotation of food, nutrition and health Arabic web content. Procedia Comput. Sci. 19, 461–469 (2013)CrossRefGoogle Scholar
  18. 18.
    Alghamdi, H.M., Selamat, A., Karim, N.S.A.: Arabic web pages clustering and annotation using semantic class features. J. King Saud Univ. Comput. Inf. Sci. 26(4), 388–397 (2014)Google Scholar
  19. 19.
    El Zraie, B.: Extraction of Taxonomic Relations from Arabic Text for Ontology Construction. Unpublished master’s thesis, Islamic University-Gaza, Deanery of Graduate Studies, Faculty of Information Technology (2016)Google Scholar
  20. 20.
    Yang, C.Y., Lin, H.Y.: Semantic annotation for the web of data - an ontology and RDF based automated approach. J. Converg. Inf. Technol. (JCIT), Special Issue Soc. Netw. Appl. Decis. Support 6(4), 318–327 (2011)Google Scholar
  21. 21.
    Manola, F., Miller, E., McBride, B.: RDF primer. W3C recommendation, 10(1–107), 6 (2004)Google Scholar
  22. 22.
    Champin, P.-A.: RDF Tutorial. Pierre-Antoine Champin, 1–9, 5 April 2001Google Scholar
  23. 23.
    Alatrash, E.: Using Web Tools for Constructing an Ontology of Different Natural Languages, Doctoral dissertation, University of Belgrade (2013)Google Scholar
  24. 24.
    Corcho, O., Fernández-López, M., Gómez-Pérez, A.: Methodologies, tools and languages for building ontologies. Where is their meeting point? Data Knowl. Eng. 46(1), 41–64 (2003)CrossRefGoogle Scholar
  25. 25.
    Pan, J., Horrocks, I.: RDFS(FA): connecting RDF(S) and OWL DL. IEEE Trans. Knowl. Data Eng. 19, 192–206 (2007)CrossRefGoogle Scholar
  26. 26.
    Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)CrossRefGoogle Scholar
  27. 27.
    Ahmed, Z.: Domain Specific Information Extraction for Semantic Annotation. (Unpublished Master Thesis), Charles University (2009)Google Scholar
  28. 28.
    Al Tayyar, M.S.: Arabic information retrieval system based on orphological analysis (AIRSMA). Ph.D. Thesis DeMonfort University, July 2000Google Scholar
  29. 29.
    López-Pellicer, F.J., Vilches-Blázquez, L.M., Nogueras-Iso, J., Corcho, Ó., Bernabé, M.A., Rodríguez, A.F.: Using a hybrid approach for the development of an ontology in the hydrographical domain (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer DepartmentDamietta UniversityDamiettaEgypt
  2. 2.Computer Science DepartmentPort Said UniversityPort SaidEgypt

Personalised recommendations