Local versus global link information in the Web

Author:

Calado Pável1,Ribeiro-Neto Berthier1,Ziviani Nivio1,Moura Edleno2,Silva Ilmério3

Affiliation:

1. Federal University of Minas Gerais, Belo Horizonte, MG, Brazil

2. Akwan Information Technologies

3. Federal University of Uberlândia

Abstract

Information derived from the cross-references among the documents in a hyperlinked environment, usually referred to as link information, is considered important since it can be used to effectively improve document retrieval. Depending on the retrieval strategy, link information can be local or global. Local link information is derived from the set of documents returned as answers to the current user query. Global link information is derived from all the documents in the collection. In this work, we investigate how the use of local link information compares to the use of global link information. For the comparison, we run a series of experiments using a large document collection extracted from the Web. For our reference collection, the results indicate that the use of local link information improves precision by 74%. When global link information is used, precision improves by 35%. However, when only the first 10 documents in the ranking are considered, the average gain in precision obtained with the use of global link information is higher than the gain obtained with the use of local link information. This is an interesting result since it provides insight and justification for the use of global link information in major Web search engines, where users are mostly interested in the first 10 answers. Furthermore, global information can be computed in the background, which allows speeding up query processing.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,General Business, Management and Accounting,Information Systems

Reference39 articles.

1. Anderson T. W. and Finn J. D. 1997. The New Statistical Analysis of Data 1st ed. Springer-Verlag New York. Anderson T. W. and Finn J. D. 1997. The New Statistical Analysis of Data 1st ed. Springer-Verlag New York.

2. Baeza-Yates R. and Ribeiro-Neto B. 1999. Modern Information Retrieval 1st ed. Addison-Wesley-Longman Reading MA. Baeza-Yates R. and Ribeiro-Neto B. 1999. Modern Information Retrieval 1st ed. Addison-Wesley-Longman Reading MA.

Cited by 24 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Information and data management at PUC-rio and UFMG;Proceedings of the VLDB Endowment;2018-08

2. A Free Access, Automated Law Citator with International Scope: The LawCite Project;SSRN Electronic Journal;2016

3. Literature-related discovery: common factors for Parkinson’s Disease and Crohn’s Disease;Scientometrics;2014-05-11

4. Using site-level connections to estimate link confidence;Journal of the American Society for Information Science and Technology;2012-10-16

5. LePrEF: Learn to precompute evidence fusion for efficient query evaluation;Journal of the American Society for Information Science and Technology;2012-06

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3