A novel factoid ranking model for information retrieval

  • Youcong Ni*
  • , Wei Wang
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

How can we distinguish accurate information from inaccurate or untrustworthy information is a big challenge in the field of information retrieval. This paper discusses trust as a factoid learning problem, which extracts factoid from the content and then rank them according to their likehood as trustworthy ones. Learning methods for performing factoid ranking are proposed in this paper, and then we combine our method with the famous PageRank algorithm to form a more powerful method for retrieval reliable information. Evaluating of the model and the experimental results were presented.

Original languageEnglish
Title of host publicationAdvances in Web and Network Technologies, and Information Management - APWeb/WAIM 2007 International Workshops DBMAN 2007, WebETrends 2007, PAIS 2007 and ASWAN 2007, Proceedings
PublisherSpringer Verlag
Pages308-316
Number of pages9
ISBN (Print)9783540729082
DOIs
StatePublished - 2007
Externally publishedYes
EventApWeb/WAIM 2007 International Workshops: 1st International workshop on Database Management and Applications over Networks, DBMAN 2007 - 1st Workshop on Emerging Trends of Web Technologies and Applications, WebETrends 2007 - International Workshop on - Huang Shan, China
Duration: 16 Jun 200718 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4537 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceApWeb/WAIM 2007 International Workshops: 1st International workshop on Database Management and Applications over Networks, DBMAN 2007 - 1st Workshop on Emerging Trends of Web Technologies and Applications, WebETrends 2007 - International Workshop on
Country/TerritoryChina
CityHuang Shan
Period16/06/0718/06/07

Keywords

  • Information retrieval
  • Ranking, web mining
  • SVM

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