中国科学院软件研究所机构知识库
Advanced  
ISCAS OpenIR  > 软件所图书馆  > 会议论文
Title:
LSG: A unified multi-dimensional latent semantic graph for personal information retrieval
Author: Huangfu, Yang (1) ; Liu, Kuien (1) ; Zhang, Wen (1) ; Zhou, Peng (1) ; Wu, Yanjun (1) ; Wang, Qing (1) ; Zhu, Jia (4)
Conference Name: 15th International Conference on Web-Age Information Management, WAIM 2014
Conference Date: June 16, 2014 - June 18, 2014
Issued Date: 2014
Conference Place: Macau, China
Keyword: Latent Semantic Discovery ; Graph Model ; Information Retrieval
Publish Place: Springer Verlag
Indexed Type: CPCI ; EI
ISSN: 3029743
ISBN: 9783319080093
Department: (1) Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China; (2) University of Chinese Academy of Sciences, Beijing, 100190, China; (3) State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China; (4) School of Computer Science, South China Normal University, Guangzhou, 510631, China
Abstract: Traditional desktop search engines can merely support keywordbased search as they don't utilize any other information, such as contextual/ semantic information, which has been commonly used in internet search. We observe that a user usually operates some files to complete a task related to a certain topic and organizes these files in some directories. Inspired by the observation, we propose an approach that considers three relations among personal files to improve desktop search, namely Topic, Task and Location. Each relation is derived from topics of files, user activities log and hierarchy of file system respectively. The heart of our approach is Latent Semantic Graph (LSG), which can measure the three relations with associated score. Based on LSG, we develop a personalized ranking schema to improve traditional keyword- based desktop search and design a novel recommendation algorithm to expand search results semantically. Experiments reveal that the performance of proposed approach is superior to that of traditional keyword-based desktop search. © 2014 Springer International Publishing Switzerland.
English Abstract: Traditional desktop search engines can merely support keywordbased search as they don't utilize any other information, such as contextual/ semantic information, which has been commonly used in internet search. We observe that a user usually operates some files to complete a task related to a certain topic and organizes these files in some directories. Inspired by the observation, we propose an approach that considers three relations among personal files to improve desktop search, namely Topic, Task and Location. Each relation is derived from topics of files, user activities log and hierarchy of file system respectively. The heart of our approach is Latent Semantic Graph (LSG), which can measure the three relations with associated score. Based on LSG, we develop a personalized ranking schema to improve traditional keyword- based desktop search and design a novel recommendation algorithm to expand search results semantically. Experiments reveal that the performance of proposed approach is superior to that of traditional keyword-based desktop search. © 2014 Springer International Publishing Switzerland.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16516
Appears in Collections:软件所图书馆_会议论文

Files in This Item:

There are no files associated with this item.


Recommended Citation:
Huangfu, Yang ,Liu, Kuien ,Zhang, Wen ,et al. LSG: A unified multi-dimensional latent semantic graph for personal information retrieval[C]. 见:15th International Conference on Web-Age Information Management, WAIM 2014. Macau, China. June 16, 2014 - June 18, 2014.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Huangfu, Yang (1)]'s Articles
[Liu, Kuien (1)]'s Articles
[Zhang, Wen (1)]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Huangfu, Yang (1)]‘s Articles
[Liu, Kuien (1)]‘s Articles
[Zhang, Wen (1)]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2019  中国科学院软件研究所 - Feedback
Powered by CSpace