2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 3 , Issue 2 , PP: 43-56, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
M.M.El-Gayar 1 * , M. EL-Hasnony 2
Doi: https://doi.org/10.54216/JISIoT.030201
The spread of Internet sources has increased the volume of big data that is difficult to handle in traditional ways. So, most users need modern search systems to facilitate the search and retrieval of information in the presence of big data. However, the main challenge in the first and second conventional generations of search engines are linking different web data based on the syntax of keywords not on the semantic meaning and without a knowledge base. This manuscript proposes a framework based on modern technologies such as ETI processes, ontology graphs, and indexing RDF using wide column NoSQL technique. The main contribution of our work is introducing a mathematical model that is used to calculate the similarity score between a query and stored RDF documents based on semantic relations. Various operations were carried out to measure the proposed model's efficiency using data sources such as DBpedia, YAGO dataset. According to experimental results, the proposed model is achieving high precision compared to other related systems.
Search Engine, Big Data, Ontology, Semantic Web, NoSQL
1. C. Gavankar, T. Bhosale, D. Gunda, A. Chavan and S. Hassan, "A Comparative Study of Semantic Search Systems," 2020 International Conference on Computer Communication and Informatics (ICCCI), 2020, pp. 1-7
2. Z. Pan, "Optimization of Information Retrieval Algorithm for Digital Library Based on Semantic Search Engine," 2020 International Conference on Computer Engineering and Application (ICCEA), 2020, pp. 364-367
3. A. Dramilio, C. Faustine, S. Sanjaya and B. Soewito, "The Effect and Technique in Search Engine Optimization," 2020 International Conference on Information Management and Technology (ICIMTech), 2020, pp. 348-353
4. A. Nadeem, M. Hussain and A. Iftikhar, "New Technique to Rank Without Off Page Search Engine Optimization," 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, pp. 1-6
5. M. M. El-Gayar, N. E. Mekky, A. Atwan and H. Soliman, "Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations," in International journal of simulation: systems, science & technology, vol. 10, 2019
6. M. M. El-Gayar, N. E. Mekky, A. Atwan and H. Soliman, " A Novel Knowledge-based Semantic Search Engine," in IEEE Access, vol. 7, pp. 139337-139349, 2019
7. M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R.Wald, and E. Muharemagic, “Deep learning applications and challenges in big data analytics,” J. Big Data, vol. 2, no. 1, Dec.2015.
8. Y. S. Negi and S. Kumar, “A comparative analysis of keywordand semantic-based search engines,” in Advances in Intelligent Systems and Computing, 2014, vol. 243, pp. 727–736.
9. J. M. Kassim and M. Rahmany, “Introduction to semantic search engine,” in Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009, 2009, vol. 2, pp. 380–386.
10. A. Begdouri, O. Chergui, and D. Leclet-Groux, “A knowledge-based approach for keywords modeling into a semantic graph,” 2018. “OWL,” in Semantic Web: Concepts, Technologies and Applications, London: Springer London, pp. 81–103.
11. M. N. Asim, M. Wasim, M. U. G. Khan, N. Mahmood, and W. Mahmood, “The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval,” IEEE Access, vol. 7, pp. 21662–21686, 2019.
12. B. R. Prasad and S. Agarwal, “Comparative Study of Big Data Computing and Storage Tools: A Review,” Int. J. Database Theory Appl., vol. 9, no. 1, pp. 45–66, Jan. 2016.
13. A. Oussous, F. Z. Benjelloun, A. Ait Lahcen, and S. Belfkih, “Big Data technologies: A survey,” Journal of King Saud University - Computer and Information Sciences, vol. 30, no. 4. King Saud bin Abdulaziz University, pp. 431–448, 01-Oct-2018.
14. D. Vohra and D. Vohra, “Using Apache HBase,” in Pro Docker, Apress, 2016, pp. 141–150.
15. K. Shvachko, H. Kuang, S. Radia, and R. Chansler, “The Hadoop distributed file system,” in 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies, MSST2010, 2010.
16. S. Shahrivari, “Beyond batch processing: Towards real-time and streaming big data,” Computers, vol. 3, no. 4. MDPI AG, pp. 117– 129, 01-Dec-2014.
17. D. Singh and C. K. Reddy, “A survey on platforms for big data analytics,” J. Big Data, vol. 2, no. 1, Dec. 2015.
18. V. Bevilacqua, V. Santarcangelo, A. Magarelli, A. Bianco, G. Mastronardi, and E. Cascini, “A semantic search framework for document retrievals (literature, art and history) based on Thesaurus multi wordnet,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6838 LNCS, pp. 456–463.
19. Y. Qu and G. Cheng, “Falcons concept search: A practical search engine for web ontologies,” IEEE Trans. Syst. Man, Cybern. Part Systems Humans, vol. 41, no. 4, pp. 810–816, Jul. 2011.
20. L. Ding et al., “Swoogle Bibliographic Search,” Proceedings of the Thirteenth ACM conference on Information and knowledge management - CIKM ’04. ACM Press, New York, New York, USA, p. 652, 2004.
21. A. Hogan, A. Harth, J. Umbrich, S. Kinsella, A. Polleres, and S. Decker, “Searching and browsing Linked Data with SWSE: The Semantic Web Search Engine,” J. Web Semant., vol. 9, no. 4, pp. 365–401, Dec. 2011.
22. S. S. Laddha and P. M. Jawandhiya, “Semantic tourism information retrieval interface,” in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017, vol. 2017–January, pp. 694–697.
23. A. Fatima, C. Luca, and G. Wilson, “New framework for semantic search engine,” in Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014, 2014, pp. 446–451.
24. M. Färber, F. Bartscherer, C. Menne, and A. Rettinger, “Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO,” Semant. Web, vol. 9, no. 1, pp. 77–129, 2018.
