A Robust Framework for Web Information Extraction and Retrieval

The large volume of online and offline information that is available today has overwhelmed users' efficiency and effectiveness in processing this information in order to extract relevant information. The exponential growth of the volume of Internet information complicates information access. Th...

Full description

Saved in:
Bibliographic Details
Published in:International journal of machine learning and computing 2014-04, Vol.4 (2), p.146-150
Main Authors: Alfred, Rayner, Soon, Gan Kim, On, Chin Kim, Anthony, Patricia
Format: Article
Language:English
Subjects:
Artificial intelligence
Data sources
Expert systems
Information retrieval
Internet
Machine learning
Queries
Tasks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The large volume of online and offline information that is available today has overwhelmed users' efficiency and effectiveness in processing this information in order to extract relevant information. The exponential growth of the volume of Internet information complicates information access. Thus, it is a very time consuming and complex task for user in accessing relevant information. Information retrieval (IR) is a branch of artificial intelligence that tackles the problem of accessing and retrieving relevant information. The aim of IR is to enable the available data source to be queried for relevant information efficiently and effectively. This paper describes a robust information retrieval framework that can be used to retrieve relevant information. The proposed information retrieval framework is designed to assist users in accessing relevant information effectively and efficiently as it handles queries based on user preferences. Each component and module involved in the proposed framework will be explained in terms of functionality and the processes involved.
ISSN:2010-3700
2010-3700
DOI:10.7763/IJMLC.2014.V4.403