LSG: A unified multi-dimensional latent semantic graph for personal information retrieval

ISCAS OpenIR

	LSG: A unified multi-dimensional latent semantic graph for personal information retrieval
	Huangfu, Yang (1); Liu, Kuien (1); Zhang, Wen (1); Zhou, Peng (1); Wu, Yanjun (1); Wang, Qing (1); Zhu, Jia (4)
	2014
会议名称	15th International Conference on Web-Age Information Management, WAIM 2014
页码	540-552
会议日期	June 16, 2014 - June 18, 2014
会议地点	Macau, China
收录类别	CPCI ; EI
出版地	Springer Verlag
ISSN	3029743
ISBN	9783319080093
部门归属	(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
摘要	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.; 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.
关键词	Latent Semantic Discovery Graph Model Information Retrieval
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16516
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Huangfu, Yang ,Liu, Kuien ,Zhang, Wen ,et al. LSG: A unified multi-dimensional latent semantic graph for personal information retrieval[C]. Springer Verlag,2014:540-552.