Open Access   Article Go Back

Implementation of Web Content Extraction of Structured Data Using DotNet Framework

M.Florence Dayana1 , Dr.M.Chidambaram 2

  1. Department of Computer Science, A.V.V.M Sri Pushpam College (Autonomous), Poondi, Thanjavur, India.
  2. Department of Computer Science, Rajah Serfoji Government College (Autonomous), Thanjavur, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 659-663, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.659663

Online published on May 31, 2018

Copyright © M.Florence Dayana, Dr.M.Chidambaram . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: M.Florence Dayana, Dr.M.Chidambaram, “Implementation of Web Content Extraction of Structured Data Using DotNet Framework,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.659-663, 2018.

MLA Style Citation: M.Florence Dayana, Dr.M.Chidambaram "Implementation of Web Content Extraction of Structured Data Using DotNet Framework." International Journal of Computer Sciences and Engineering 6.5 (2018): 659-663.

APA Style Citation: M.Florence Dayana, Dr.M.Chidambaram, (2018). Implementation of Web Content Extraction of Structured Data Using DotNet Framework. International Journal of Computer Sciences and Engineering, 6(5), 659-663.

BibTex Style Citation:
@article{Dayana_2018,
author = {M.Florence Dayana, Dr.M.Chidambaram},
title = {Implementation of Web Content Extraction of Structured Data Using DotNet Framework},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {6},
Issue = {5},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {659-663},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2038},
doi = {https://doi.org/10.26438/ijcse/v6i5.659663}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.659663}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2038
TI - Implementation of Web Content Extraction of Structured Data Using DotNet Framework
T2 - International Journal of Computer Sciences and Engineering
AU - M.Florence Dayana, Dr.M.Chidambaram
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 659-663
IS - 5
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
416 286 downloads 230 downloads
  
  
           

Abstract

This paper deals in Web Content Mining for extraction of structured data. While perusing the web, the client needs to experience numerous pages of the Internet, channel the information and download related records and documents. This errand of seeking and downloading is tedious. Now and again the look inquiries call for particular choice, say, restricting inquiry to few connections. To lessen the time spent by clients, a web extraction and capacity apparatus has been composed and executed in .Net framework, that robotizes the downloading task from a given client question. The Test Scenario has been given different catchphrases. The present work can be a valuable contribution to Web Manipulators, Staff, Students and Web Administrators in an Academic Environment.

Key-Words / Index Term

Web Content Mining, Structured Data, Web Data Extraction, HTML, Data mining, Web Mining

References

[1] U. Moulali, V. Sasidhar, “Competent pattern innovation designed for textual content mining”, 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 572 – 577.
[2] Farman Ali, Pervez Khan, Kashif Riaz, Daehan Kwak, Tamer Abuhmed, Daeyoung Park, Kyung Sup Kwak, “A Fuzzy Ontology and SVM–Based Web Content Classification System”, IEEE Access, Vol. 5, pp. 25781 – 25797.
[3] Yeongsu Kim, Seungwoo Lee, “SVM-based web content mining with leaf classification unit from DOM-tree”, 2017 9th International Conference on Knowledge and Smart Technology (KST), pp. 359 – 364.
[4] Tak-Lam Wong, Wai Lam, “Learning to Adapt Web Information Extraction Knowledge and Discovering New Attributes via a Bayesian Approach” IEEE Trans. on Knowledge and Data Engineering, Vol. 22, No. 4, pp. 523 – 536, 2010.
[5] Charu C. Aggarwal, Yuchen Zhao, Philip S. Yu, “On the Use of Side Information for Mining Text Data”, IEEE Trans. on Knowledge and Data Engineering, Vol. 26, No. 6, pp. 1415 – 1429, 2014.
[6] Kaveh Hassani, Won-Sook Lee, “Adaptive animation generation using web content mining”, 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1 – 8.
[7] G. Dhivya, K. Deepika, J. Kavitha, V. Nithya Kumari, “Enriched content mining for web applications”, 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1 – 5.
[8] Tao Jiang, Ah-hwee Tan, Ke Wang, “Mining Generalized Associations of Semantic Relations from Textual Web Content”, IEEE Trans. on Knowledge and Data Engineering, Vol. 19, No. 2, pp. 164 – 179.
[9] Hung-Yu Kao, Shian-Hua Lin, Jan-Ming Ho, Ming-Syan Chen, “Mining Web informative structures and contents based on entropy analysis”, IEEE Trans. on Knowledge and Data Engineering, Vol. 16, No. 1, pp. 41 – 55, 2004.
[10] F. de la Rosa Troyano, S. del Pozo Hidalgo, R. Martinez Gasca, “Analysis and Visualization of Scientific Communities with Information Extracted from the Web”, IEEE Latin America Transactions, Vol. 3, No. 1, pp. 56 – 61.
[11] I-Jen Chiang, Charles Chih-Ho Liu, Yi-Hsin Tsai, Ajit Kumar, “Discovering Latent Semantics in Web Documents Using Fuzzy Clustering”, IEEE Trans. on Fuzzy Systems, Vol. 23, No. 6, pp. 2122 – 2134.
[12] Hao Ma, Irwin King, Michael R. Lyu, “Mining Web Graphs for Recommendations”, IEEE Trans. on Knowledge and Data Engineering, Vol. 24, No. 6, pp. 1051 – 1064, 2012.
[13] Tak-Lam Wong, Wai Lam, “Learning to Adapt Web Information Extraction Knowledge and Discovering New Attributes via a Bayesian Approach”, IEEE Trans. on Knowledge and Data Engineering, Vol. 22, No. 4, pp. 523 – 536.
[14] Wei Liu, Xiaofeng Meng, Weiyi Meng, “ViDE: A Vision-Based Approach for Deep Web Data Extraction”, IEEE Trans. on Knowledge and Data Engineering, Vol. 22, No. 3, pp. 447 – 460.