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  State of the Art in Semantic Web Search Techniques for Arabic Language  
  Authors : Ruqaia Jwad   Dr.Norita Md Norwawi    Bala Musa
  Cite as: ijcsn.org/IJCSN-2013/2-5/IJCSN-2013-2-5-14.pdf

 

Arabic language has many differences from English language in terms of morphology and semantic. These areas of difference make it somehow difficult when it comes to web search in Arabic. Unlike Arabic language, other languages including Latin have substantiated amount of research in the use of semantic technologies in processing text and information retrieval. Despite the complexity in Arabic script, some significantly contribution has been made in the area of retrieval algorithms and semantic web techniques which can be measured in terms of the accuracy. This paper therefore, examines the state of the art in the use of semantic web search techniques for the retrieval of Arabic text.

 

Published In : IJCSN Journal Volume 2, Issue 5

Date of Publication : 01 October 2013

Pages : 05 - 07

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Publication Link : ijcsn.org/IJCSN-2013/2-5/IJCSN-2013-2-5-14.pdf

 

 

 

Ruqaia Jwad : Faculty of Science and Technology, Universiti Sains Islam Malaysia, Bander Baru Nilai, Malaysia.

Dr.Norita Md Norwawi : Faculty of Science and Technology, Universiti Sains Islam Malaysia, Bander Baru Nilai, Malaysia.

Bala Musa : Faculty of Science and Technology, Universiti Sains Islam Malaysia, Bander Baru Nilai, Malaysia.

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Semantic Web

Arabic Ontology

Natural Language Processing

Arabic Search

 

 

 

 

 

 

Enormous effort has been put in place to facilitate efficient and effective use of techniques for Arabic language text retrieval. These efforts are still not standardized and still huge gap is yet to be filled in terms of Arabic retrieval. Therefore, there is need for an efficient and effective approach that will incorporate the best techniques and eliminate the current impediments associated with Arabic text retrieval.

 

 

 

 

 

 

 

 

[1] M.Abu-Hamdiyyah, and I. ebrary. The Qur'an: an introduction, 2000,Taylor & Francis.

[2] Q. A Al-Radaideh,. and K. H. Masri. Improving mobile multi-tap text entry for Arabic language. 2011 Computer Standards & Interfaces 33(1): 108-113. Al- Shammari, E. and J. Lin 2008. A novel Arabic lemmatization algorithm, ACM.

[3] S. R.El-Beltagy, and A. Rafea. KP-Miner: A keyphrase extraction system for English and Arabic documents. 2009. Information Systems 34(1): 132-144.

[4] M.El Kourdi, , A. Bensaid, et al. Automatic Arabic document categorization based on the Naïve Bayes algorithm. 2004.

[5] A.Goweder, , M. Poesio, et al. Broken plural detection for arabic information retrieval. 2004. ACM.

[6] O.Isbaitan, and H. Al-Wahidi Arabic model for semantic web 3.0. Proceedings of the 2011 International Conference on Intelligent Semantic Web-Services and Applications. 2011.Amman, Jordan, ACM: 1-6.

[7] T.Kanungo, , G. A. Marton, et al. OmniPage vs. Sakhr: Paired model evaluation of two Arabic OCR products, 1999.SPIE INTERNATIONAL SOCIETY FOR OPTICAL.

[8] S.Khoja, APT: Arabic part-of-speech tagger. 2001.

[9] M. R.Koivunen. W3C semantic web activity. Semantic Web .2001.KickOff in Finland: 27-41.

[10] Nana Yaw Asabere, Nana Kwame Gyamfi, AIDSS-HR: An Automated Intelligent Decision Support System for Enhancing the Performance of Employees, arXiv:1307.8335.

[11] A.Selamat, and C. C. Ng. Arabic script web page language identifications using decision tree neural networks. 2011. Pattern Recognition 44(1): 133-144.

[12] K.Shaalan, and H. Raza . Person name entity recognition for Arabic, Association for Computational Linguistics. 2007.

[13] S Zaidi,., M. Laskri, et al. A cross-language information retrieval based on an Arabic ontology in the legal domain. 2005.