Earticle

초록

영어

Current search engines require an accurate yet fast automated extractor to extract relevant information from deep web for the users. Human users usually enter search queries and the search engines will then locate the desire information of interest by disambiguate the search query accordingly. The queries will then be passed on to multiple search engines for further processing. These search engines will then return the search results to the main search engine. However, data returned from these search engines are usually varied and presented in numerous formats and layouts. To extract them, we need automated extractor to filter out irrelevant information and locate the correct information. Current trends focused on using ontologies to automatically extract this information with high accuracy. To the best of our knowledge, no works have been made on using multiple ontologies (using many ontology techniques) to automatically extract information from deep webs. In this paper, we demonstrate that multiple ontologies technique can achieve higher accuracy when extracting data from the deep web. Our method outperforms existing state of the art systems and is able to robustly extract data from deep web.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1 Current Extraction Tools
  2.2 Ontological based Extractors
  2.3 State of the art Ontology Tools
 3. Proposed Methodology
  3.1 Overview
  3.2 Region Detection
  3.3 Extraction using WordNet
  3.4 Extraction using CYC
  3.5 Extraction using Wikitology
  3.6 Filtering of Regions
 4. Experimental Test
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Jer Lang Hong [ School of Computing and IT, Taylor’s University, Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605