Machine learning for information architecture in a large governmental website

Miles James Efron, Jonathan Elsas, Gary Marchionini, Junliang Zhang

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

Abstract

This paper describes ongoing research into the application of machine learning techniques for improving access to governmental information in complex digital libraries. Under the auspices of the GovStat Project, our goal is to identify a small number of semantically valid concepts that adequately spans the intellectual domain of a collection. The goal of this discovery is twofold. First we desire a practical aid for information architects. Second, automatically derived document-concept relationships are a necessary precondition for real-world deployment of many dynamic interfaces. The current study compares concept learning strategies based on three document representations: keywords, titles, and full-text. In statistical and user-based studies, human-created keywords provide significant improvements in concept learning over both title-only and full-text representations.

Original languageEnglish (US)
Title of host publicationProceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries; Global Reach and Diverse Impact, JCDL 2004
EditorsH. Chen, M. Christel, E.P. Lim
Pages151-159
Number of pages9
StatePublished - Oct 18 2004
EventProceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries; Global reach and Diverse Impact, JCDL 2004 - Tucson, AZ, United States
Duration: Jun 7 2004Jun 11 2004

Publication series

NameProceedings of the ACM IEEE International Conference on Digital Libraries, JCDL 2004

Other

OtherProceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries; Global reach and Diverse Impact, JCDL 2004
CountryUnited States
CityTucson, AZ
Period6/7/046/11/04

Keywords

  • Information Architecture
  • Interface Design
  • Machine Learning

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

Fingerprint Dive into the research topics of 'Machine learning for information architecture in a large governmental website'. Together they form a unique fingerprint.

Cite this