Tag Based Relational Web Annotation Approach For Decision Making
||International Journal of Computer & Organization Trends (IJCOT)||
|© 2014 by IJCOT Journal|
|Volume - 4 Issue - 6
|Year of Publication : 2014|
|Authors : Ponnuru Nalini, M.Mohana Deepthi|
|DOI : 10.14445/22492593/IJCOT-V15P306|
Ponnuru Nalini, M.Mohana Deepthi "Tag Based Relational Web Annotation Approach For Decision Making", International Journal of Computer & organization Trends (IJCOT), V4(6):6-11 Nov - Dec 2014, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
A fresh web content structure in accordance to visual illustration is proposed in this paper. Many web applications which can include information retrieval, information extraction and automatic web web page adaptation may benefit from this structure. Then it extracts each record beginning with the data areas and identifies it no matter if it is toned slimmer slim trim slendor thin or nested documents in accordance to visual information towards the realm covered plus the large number of data items comprised in each record. The next thing is data items extraction by reviewing them documents and transferring them into the database.This paper includes an automatic topdown, tagtree independent method of detect web structure content. It iterates and extracts how the web user understands web structure based upon his visual perception. Contrasting to other traditional techniques, our approach is independent to hidden documentation illustration namely. Today’s web structure content is best, if the HTML structure is far separate from layout structure.
H. He, W. Meng, C. Yu, and Z. Wu, “Automatic Integration of Web Search Interfaces with WISE-Integrator,” VLDB J., vol. 13, no. 3, pp. 256-273, Sept. 2004.
 W. Liu, X. Meng, and W. Meng, “ViDE: A Vision-Based Approach for Deep Web Data Extraction,” IEEE Trans. Knowledge and Data Eng., vol. 22, no. 3, pp. 447-460, Mar. 2010.
 W. Su, J. Wang, and F.H. Lochovsky, “ODE: Ontology-Assisted Data Extraction,” ACM Trans. Database Systems, vol. 34, no. 2, article 12, June 2009.
 W. Meng, C. Yu, and K. Liu, “Building Efficient and Effective Metasearch Engines,” ACM Computing Surveys, vol. 34, no. 1, pp. 48-89, 2002.
 D. Embley, D. Campbell, Y. Jiang, S. Liddle, D. Lonsdale, Y. Ng, and R. Smith, “Conceptual-Model-Based Data Extraction from Multiple-Record Web Pages,” Data and Knowledge Eng., vol. 31,no. 3, pp. 227-251, 1999.
 Adelberg, B., NoDoSE: A tool for semi-automatically extracting structured and semi-structured data from text documents. SIGMOD Record 27(2): 283-294, 1998.
 A. Arasu and H. Garcia-Molina, “Extracting Structured Data from Web Pages,” Proc. SIGMOD Int’l Conf. Management of Data, 2003.
 L. Arlotta, V. Crescenzi, G. Mecca, and P. Merialdo, “Automatic Annotation of Data Extracted from Large Web Sites,” Proc. Sixth Int’l Workshop the Web and Databases (WebDB), 2003.
 P. Chan and S. Stolfo, “Experiments on Multistrategy Learning by Meta-Learning,” Proc. Second Int’l Conf. Information and Knowledge Management (CIKM), 1993.
 W. Bruce Croft, “Combining Approaches for Information Retrie- val,” Advances in Information Retrieval: Recent Research from the Center for Intelligent Information Retrieval, Kluwer Academic, 2000.
 V. Crescenzi, G. Mecca, and P. Merialdo, “RoadRUNNER: Towards Automatic Data Extraction from Large Web Sites,” Proc. Very Large Data Bases (VLDB) Conf., 2001.