GENAUM: NEW SEMANTIC DISTRIBUTED SEARCH ENGINE

Authors

  • ICHRAK SAIF ENSIAS, Mohammed V University Rabat, Morocco
  • ABDELAZIZ SDIGUI DOUKKALI ENSIAS, Mohammed V University Rabat, Morocco
  • ADIL ENAANAI ENSIAS, Mohammed V University Rabat, Morocco
  • EL HABIB BENLAHMAR Faculty of science Ben M’sik, Hassan II University Casablanca, Morocco

Keywords:

Web search, semantic search engine, distributed search engine, user profile, genaum

Abstract

The rapid development of services based on distributed architectures is now emerging as important items that transform mode of communication, and the exponential growth of the Web makes a strong pressure on technologies, for a regular improvement of performance, so it’s irresistible to use distributed architectures and techniques for the search and information retrieval on the Web, to provide more relevant search result, in minimum possible time. This paper discuss some solutions researchers are working on, to make search engines more faster and more intelligent, specifically by considering the semantic context of users and documents, and the use of distributed architectures. This paper also presents the overall architecture of GENAUM; the collaborative, semantic and distributed search engine, based on a network of agents, which is the core part of the system. The functionality of GENAUM is spread across multiple agents, to fulfill user’s performance expectations. At the end of this paper, some preliminary experimental results are presented, that attempts to test the user modeling process of GENAUM, using reference ontology.

 

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References

A. Spink, B.J. Jansen, C. Blakely, and S. Koshman. A study of results overlap and uniqueness among

major web search engines. Information Processing & Management, 2006. 42(5), 1379-1391.

B. B. Cambazoglu, and R. Baeza-Yates. Scalability challenges in web search engines. Advanced

topics in information retrieval, 2011. 33, 27-50.

E. D. Liddy. Enhanced text retrieval using natural language processing. Bulletin of the American

Society for Information Science, 1998. 24(4), 14-16.

J. Gantz, and D. Reinsel. The Digital Universe In 2020: Big Data, Bigger Digital Shadows, and

Biggest Growth in the Far East. Technical report. Internet Data Center(IDC), 2012.

J. Beal. Weaknesses of Full text search. The Journal of Academic Librarianship, 2008. 34(5), 438-

A. Ramachandran, and R.Sujatha. Semantic search engine: A survey. International Journal of

Computer Technology and Applications, 2011. 2(6), 1806-1811.

J.Sirisha, B.V.Subbarao, D. Kavitha, and Y. Padma. A Comprehensive Study on Generalized Search

Engines versus Semantic Search Engines. International Journal on Recent and Innovation Trends in

Computing and Communication, 2014. 2(4), 757-761.

L. Ding, T. Finin, A. Joshi, R. Pan, R. Cost, Y. Peng, P. Reddivari, V. Doshi, and J. Sachs. Swoogle:

a search and metadata engine for the semantic Web. Proceedings of the 13th ACM Conference

Information and Knowledge Management, New York, USA, 2004. 652-659.

Y. Zhang, W. Vasconcelos, and D. Sleeman, Ontosearch. An ontology search engine. Proceedings

of the 24th SGAI International Conference on Innovative techniques and Applications of Artificial

Intelligence, Washington DC - USA, 2004. 256-259.

C. Patel, K. Supekar, Y. Lee, and E. K. Park. OntoKhoj: a semantic Web portal for ontology

searching, ranking and classification. Proceedings of the 5th ACM international workshop on Web

information and data management, 2003. 58-61.

M. Shekhar and RA. K. Saravanaguru. A case study on semantic web search using ontology

modelling. International journal of engineering and technology, 2013. 5(3), 2342-2348.

V. Shah, A. Shah, and K. Deulkar. Comparative study of semantic search engines. International

Journal Of Engineering And Computer Science, 2015. 4(11), 14969-14972.

N. Chen, and C. Xiangyang. Investigation of Distributed Search Engine Based on Hadoop.

TELKOMNIKA Indonesian Journal of Electrical Engineering, 2014. 12(9), 6954-6957.

Z. Hong, M. Yan-hong , M. Wei-jun, B. Zhong-xian. Study of Distributed Personalized Search

Engine. Advanced Materials Research, 2013.756, 1035-1039.

R. GU. Research Distributed Search Engine Based on Hadoop. Proceeding of the International

Conference on Network and Information Systems for Computers, 2015. 373-375.

J. Wan, B. Wang, W. Guo, K. Chen and J. Wang. A Distributed Search Engine Based on a Reranking

Algorithm Model. Proceeding of the 10th International Conference on Computer Science

& Education, 2015. 640-644.

http://www.faroo.com/.

http://www.seeks-project.info/.

M. Herrmann, K. Ning , C.Diaz, and B. Preneel. Description of the YaCy Distributed Web Search

Engine. Technical report, KU Leuven ESAT/COSIC, iMinds, 2014.

A. ENAANAI. La méta-recherche en langue Arabe : Une approche hybride pour calculer la

pertinence documentaire. Thesis at Ecole Nationale d’Informatique et Analyse des Systèmes, Rabat,

Morocco, 2014.

H. Benlahmer, A. S. Doukkali, and A. Elouerkhaoui. A solution for data extraction by a new approach

:The method of gene/clone. Proceeding of the international conference on information and

communication technology for the Muslim world, Kuala Lumpur, Malaysia, 2006. 21-23.

M. Bouzeghoub, D. Kostadinov. Personnalisation de l’information : aperçu de l’état de l’art et

définition d’un modèle flexible de profils. Proceeding of Conférence en Recherche d'Infomations et

Applications CORIA’05, Grenoble, France, 2005. 201-218.

M. Daoud, L. Tamine-Lechani, and M. Boughanem. Using a concept-based user context for search

personalization. Proceedings of the world congress on engineering, London, UK, 2008. 293-298.

A. Strehl, J. Ghosh, and R. Mooney. Impact of similarity measures on Web-page Clustering.

Workshop on Artificial Intelligence for Web Search, Texas, USA, 2000. 58-64.

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Published

2018-03-29

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