Effectively searching maps in Web documents

Qingzhao Tan, Prasenjit Mitra, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Maps are an important source of information in archaeology and other sciences. Users want to search for historical maps to determine recorded history of the political geography of regions at different eras, to find out where exactly archaeological artifacts were discovered, etc. Currently, they have to use a generic search engine and add the term map along with other keywords to search for maps. This crude method will generate a significant number of false positives that the user will need to cull through to get the desired results. To reduce their manual effort, we propose an automatic map identification, indexing, and retrieval system that enables users to search and retrieve maps appearing in a large corpus of digital documents using simple keyword queries. We identify features that can help in distinguishing maps from other figures in digital documents and show how a Support-Vector-Machine-based classifier can be used to identify maps. We propose map-level-metadata e.g., captions, references to the maps in text, etc. and document-level metadata, e.g., title, abstract, citations, how recent the publication is, etc. and show how they can be automatically extracted and indexed. Our novel ranking algorithm weights different metadata fields differently and also uses the document-level metadata to help rank retrieved maps. Empirical evaluations show which features should be selected and which metadata fields should be weighted more. We also demonstrate improved retrieval results in comparison to adaptations of existing methods for map retrieval. Our map search engine has been deployed in an online map-search system that is part of the Blind-Review digital library system.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages162-176
Number of pages15
Volume5478 LNCS
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event31th European Conference on Information Retrieval, ECIR 2009 - Toulouse
Duration: 6 Apr 20099 Apr 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5478 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other31th European Conference on Information Retrieval, ECIR 2009
CityToulouse
Period6/4/099/4/09

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Tan, Q., Mitra, P., & Lee Giles, C. (2009). Effectively searching maps in Web documents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5478 LNCS, pp. 162-176). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5478 LNCS). https://doi.org/10.1007/978-3-642-00958-7_17