Please wait a minute...
New Technology of Library and Information Service  2008, Vol. 24 Issue (7): 47-53    DOI: 10.11925/infotech.1003-3513.2008.07.10
Current Issue | Archive | Adv Search |
Study on the Characters of Language Used in Web Searching
Lai Maosheng  Qu Peng
(Department of Information Management, Peking University, Beijing 100871, China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

This paper concerns on the characteristics of language used in the Web searching and seeks to make an exploratory study on the syntax and semantic query problems. The research mainly uses search engine query log analysis method, then compares with the results from Web questionnaire analysis. At last, the authors come to the conclusion on the characteristics of syntax, term category, assistant words, and principal words. The syntax in Web searching is very stable; users rely greatly on assistant words; the natural-language-character of query is obvious; and the query syntax is very simple. Search engines has become an helpful supplement for traditional information retrieval tools; forms unique contents, in which consists mostly entertainment and non-mainstream culture, and which differentiate search engine from traditional information retrieval tools.
Key words： Retrieval language      Searching      Log mining      User      Language usage     
Received: 07 March 2008      Published: 25 July 2008
: 

G352

 		 
Corresponding Authors: Lai Maosheng     E-mail: laims@pku.edu.cn
About author:: Lai Maosheng,Qu Peng
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors

Cite this article:

Lai Maosheng,Qu Peng. Study on the Characters of Language Used in Web Searching. New Technology of Library and Information Service, 2008, 24(7): 47-53.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2008.07.10     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2008/V24/I7/47

［1］ 王继民, 陈翀, 彭波. 大规模中文搜索引擎的用户日志分析［J］. 华南理工大学学报(自然科学版), 2004, 32(S): 1-5.
［2］ 王继民, 彭波. 搜索引擎用户访问量模型［J］. 计算机工程与应用, 2004, 40(25): 9-11,30.
［3］ 王继民, 彭波. 搜索引擎用户点击行为分析［J］. 情报学报, 2006, 25(2): 154-162.
［4］ 王继民, 孟涛. Web搜索引擎日志挖掘研究［R］. 中国人搜索行为研究实验室年度报告2006. 北京: 北京大学信息管理系, 2006: 35-48.
［5］ Jansen B J, Spink A, Saracevic T. Real Life, Real Users, and Real Needs: A study and Analysis of User Queries on the Web［J］. Information Processing and Management, 2000, 36(2): 207-227.
［6］ Spink A, Jansen B J, Koshman S. From E-sex to E-commerce: Web Search  Changes［J］. IEEE Computer, 2002, 35(3): 107-109.
［7］ Spink A, Ozmutlu H C, Ozmutlu S. Multitasking Information Seeking and Searching Processes［J］. Journal of the American Society for Information Science and Technology, 2002, 53(8): 639-652.
［8］ Ozmutlu S, Spink A, Ozmutlu H C. A Day in the Life of Web Searching: An Exploratory Study［J］. Information Processing and Management, 2004, 40(2):319-345.
［9］ Jansen B J, Spink A. How are We Searching the World Wide Web? A Comparison of Nine Search EngineTtransaction Logs［J］. Information Processing and Management, 2006, 42(1):248-263.
[1] Shan Xiaohong,Wang Chunwen,Liu Xiaoyan,Han Shengxi,Yang Juan. Identifying Lead Users in Open Innovation Community from Knowledge-based Perspectives[J]. 数据分析与知识发现, 2021, 5(9): 85-96.
[2] Xu Zengxulin, Xie Jing, Yu Qianqian. Designing New Evaluation Model for Talents[J]. 数据分析与知识发现, 2021, 5(8): 122-131.
[3] Wang Xiwei,Jia Ruonan,Wei Yanan,Zhang Liu. Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
[4] Zhang Mengyao, Zhu Guangli, Zhang Shunxiang, Zhang Biao. Grouping Microblog Users of Trending Topics Based on Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(2): 43-49.
[5] Shen Wang, Li Shiyu, Liu Jiayu, Li He. Optimizing Quality Evaluation for Answers of Q&A Community[J]. 数据分析与知识发现, 2021, 5(2): 83-93.
[6] Xi Yunjiang, Du Diedie, Liao Xiao, Zhang Xuehong. Analyzing & Clustering Enterprise Microblog Users with Supernetwork[J]. 数据分析与知识发现, 2020, 4(8): 107-118.
[7] Cai Yongming,Liu Lu,Wang Kewei. Identifying Key Users and Topics from Online Learning Community[J]. 数据分析与知识发现, 2020, 4(6): 69-79.
[8] Zheng Songyin,Tan Guoxin,Shi Zhongchao. Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts[J]. 数据分析与知识发现, 2020, 4(5): 92-104.
[9] Wang Xiwei,Zhang Liu,Huang Bo,Wei Ya’nan. Constructing Topic Graph for Weibo Users Based on LDA: Case Study of “Egypt Air Disaster”[J]. 数据分析与知识发现, 2020, 4(10): 47-57.
[10] Gang Li,Huayang Zhou,Jin Mao,Sijing Chen. Classifying Social Media Users with Machine Learning[J]. 数据分析与知识发现, 2019, 3(8): 1-9.
[11] Weicong Lu,Jian Xu. Sentiment Analysis for Online User Reviews Based on Tripartite Network[J]. 数据分析与知识发现, 2019, 3(8): 10-20.
[12] Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
[13] Shan Li,Yehui Yao,Hao Li,Jie Liu,Karmapemo. ISA Biclustering Algorithm for Group Recommendation[J]. 数据分析与知识发现, 2019, 3(8): 77-87.
[14] Lixin Xia,Jieyan Zeng,Chongwu Bi,Guanghui Ye. Identifying Hierarchy Evolution of User Interests with LDA Topic Model[J]. 数据分析与知识发现, 2019, 3(7): 1-13.
[15] Xiuxian Wen,Jian Xu. Research on Product Characteristics Extraction and Hedonic Price Based on User Comments[J]. 数据分析与知识发现, 2019, 3(7): 42-51.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn