Automatic keyword prediction using Google similarity distance

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/51801

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51801

Title:	Automatic keyword prediction using Google similarity distance
Authors:	Chen,PI;Lin,SJ
Contributors:	資訊管理學系
Keywords:	WEB SEARCH;RETRIEVAL;CONTEXT;LENGTH
Date:	2010
Issue Date:	2012-03-27 19:06:29 (UTC+8)
Publisher:	國立中央大學
Abstract:	In this paper, we present a new approach to help users using search engines without entering any keywords. What we want to do is to predict what word the users may want to search before they think about it. Most of the studies done in this field focus on how to help users enter keywords or how to re-rank the search results in order to make them more precise. Both of those methods need to establish a user behavior model and a repository in which to save the logs. In our proposed method, we use the Google similarity distance to measure keywords in the Webpage to find the potential keywords for the users. Thus, we do not need any repository. All the executions are on-line and real-time. Then, we extract all the important keywords as the potential search keywords. In this way, we can use these professional keywords to achieve precise search results. We believe that this can be useful in many areas such as e-learning and can also be used in mobile devices. (C) 2009 Elsevier Ltd. All rights reserved.
Relation:	EXPERT SYSTEMS WITH APPLICATIONS
Appears in Collections:	[Department of Information Management] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	451	View/Open

社群 sharing