Building enhanced link context by logical sitemap

Qing Yang, Zhendong Niu, Chunxia Zhang*, Sheng Huang

*Corresponding author for this work

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

Abstract

Link contexts have been applied to enrich document representation for a variety of information retrieval tasks. However, the valuable site-specific hierarchical information has not yet been exploited to enrich link contexts. In this paper, we propose to enhance link contexts by mining the underlying information organization architecture of a Web site, which is termed as logical sitemap to differ from sites supplied sitemap pages. We reconstruct a logical sitemap for a Web site by mining existing navigation elements such as menus, breadcrumbs, sitemap etc. It then enriches contexts of a link by aggregating contexts according to the hierarchical relationship in the mined logical sitemap. The experimental results show that our proposed approach can reliably construct a logical sitemap for a general site and the enriched link contexts derived from the logical sitemap can improve site-specific known item search performance noticeably.

Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management - 6th International Conference, KSEM 2013, Proceedings
PublisherSpringer Verlag
Pages36-47
Number of pages12
ISBN (Print)9783642397868
DOIs
Publication statusPublished - 2013
Event6th International Conference on Knowledge Science, Engineering and Management, KSEM 2013 - Dalian, China
Duration: 10 Aug 201312 Aug 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8041 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Knowledge Science, Engineering and Management, KSEM 2013
Country/TerritoryChina
CityDalian
Period10/08/1312/08/13

Keywords

  • link context
  • logical sitemap
  • site-specific known item search

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