MRI
MRI India Journals Vol. 2 No. 1 (2015): Volume 2 Issue 1 2015

REVIEW ON AUTOMATIC ANNOTATION OF QUERY RESULTS FROM DEEP WEB DATABASE

Authors

  • Chaitanya Bhosale
  • Sunil Rathod

DOI:

https://doi.org/10.65521/mjret.v2i1.988

Keywords:

Data extraction Data alignment automatic wrapper generation web database

Abstract

In recent years, web database extraction and annotation has received much attention from the database and Information Extraction(IE) in research area due to the volume and quality of deep web. Many web databases are accessible through HTML formbased interface. When query is submitted to the search interface the query result page is generated. Search Result Records(SRRs) are the result pages obtained from web database(WDB) and these SRRs are used to display the result for each query. Every SRR contains multiple data units equivalent to one semantic. These search results can be utilized in other web applications such as comparison shopping, data integration,metaquerying. To make these applications successful the search pages should be annotated in a meaningful fashion. To make it effortless for human,an automatic annotation approach is suggested. Within this,we first groups the data units of result records so that the information in the group have the same meaning. After that we annotate each group in different aspects and obtain the final annotation after aggregating them. In addition,we use a new CTVS technique for extraction of QRRs from a query result page,in which we use optional labeling and dynamic tagging for the improvement. Further an annotation wrapper is generated automatically which can be used for annotation new result records from the same WDB.

Downloads

Published

2015-01-01

How to Cite

Bhosale, C., & Rathod, S. (2015). REVIEW ON AUTOMATIC ANNOTATION OF QUERY RESULTS FROM DEEP WEB DATABASE. Multidisciplinary Journal of Research in Engineering and Technology, 2(1), 359–364. https://doi.org/10.65521/mjret.v2i1.988

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.